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Web Mining is the process of Data Mining techniques to automatically discover and extract information from Web documents and services. The main purpose of web mining is to discover useful information from the World Wide Web and its usage patterns. 

What is Web Mining?

Web mining is the best type of practice for sifting through the vast amount of data in the system that is available on the World Wide Web to find and extract pertinent information as per requirements. One unique feature of web mining is its ability to deliver a wide range of required data types in the actual process. There are various elements of the web that lead to diverse methods for the actual mining process. For example, web pages are made up of text; they are connected by hyperlinks in the system or process; and web server logs allow for the monitoring of user behavior to simplify all the required systems. Combining all the required methods from data mining, machine learning, artificial intelligence, statistics, and information retrieval, web mining is an interdisciplinary field for the overall system. Analyzing user behavior and website traffic is the one basic type or example of web mining.

Applications of Web Mining

Web mining is the process of discovering patterns, structures, and relationships in web data. It involves using data mining techniques to analyze web data and extract valuable insights. The applications of web mining are wide-ranging and include:

• Personalized marketing :Web mining can be used to analyze customer behavior on websites and social media platforms. This information can be used to create personalized marketing campaigns that target customers based on their interests and preferences.
• E-commerce: Web mining can be used to analyze customer behavior on e-commerce websites. This information can be used to improve the user experience and increase sales by recommending products based on customer preferences.
• Search engine optimization:  Web mining can be used to analyze search engine queries and search engine results pages (SERPs). This information can be used to improve the visibility of websites in search engine results and increase traffic to the website.
• Fraud detection:  Web mining can be used to detect fraudulent activity on websites. This information can be used to prevent financial fraud, identity theft, and other types of online fraud.
• Sentiment analysis: Web mining can be used to analyze social media data and extract sentiment from posts, comments, and reviews. This information can be used to understand customer sentiment towards products and services and make informed business decisions.
• Web content analysis:  Web mining can be used to analyze web content and extract valuable information such as keywords, topics, and themes. This information can be used to improve the relevance of web content and optimize search engine rankings.
• Customer service:  Web mining can be used to analyze customer service interactions on websites and social media platforms. This information can be used to improve the quality of customer service and identify areas for improvement.
• Healthcare:  Web mining can be used to analyze health-related websites and extract valuable information about diseases, treatments, and medications. This information can be used to improve the quality of healthcare and inform medical research.

Process of Web Mining

Web Mining Process

Web mining can be broadly divided into three different types of techniques of mining: Web Content Mining, Web Structure Mining, and Web Usage Mining. These are explained as following below.

Categories of Web Mining

• Web Content Mining: Web content mining is the application of extracting useful information from the content of the web documents. Web content consist of several types of data – text, image, audio, video etc. Content data is the group of facts that a web page is designed. It can provide effective and interesting patterns about user needs. Text documents are related to text mining, machine learning and natural language processing. This mining is also known as text mining. This type of mining performs scanning and mining of the text, images and groups of web pages according to the content of the input.
• Web Structure Mining: Web structure mining is the application of discovering structure information from the web. The structure of the web graph consists of web pages as nodes, and hyperlinks as edges connecting related pages. Structure mining basically shows the structured summary of a particular website. It identifies relationship between web pages linked by information or direct link connection. To determine the connection between two commercial websites, Web structure mining can be very useful.
• Web Usage Mining: Web usage mining is the application of identifying or discovering interesting usage patterns from large data sets. And these patterns enable you to understand the user behaviors or something like that. In web usage mining, user access data on the web and collect data in form of logs. So, Web usage mining is also called log mining.

Challenges of Web Mining

• Complexity of required web pages: Basically, there is no cohesive framework throughout the site’s pages so when compared to conventional text, they are incredibly intricate in the process. The web’s digital library contains a vast number of documents in the actual system. There is no set order in which these libraries are typically arranged for the user.
• Dynamic data source in the internet: The required online data is updated in real time. For instance, news, weather, fashion, finance, sports, and so forth is not possible to indicate properly.
• Data relevancy: It is much believed that a particular person is typically only concerned with a limited percentage of the internet throughout the process, with the remaining portion containing data that may provide unexpected outcomes for the actual requirement and is unfamiliar to the user to verify.
• Too much large web: Basically, the web is getting bigger and bigger very quickly in the system. The web seems to be too big for data mining and data warehousing as per requirement.

Comparison between Data Mining and Web Mining

The actual technique of finding patterns and gaining knowledge for the system requirements from web data is known as web mining. It is employed in many different fields as per need, including fraud detection, e-commerce, and marketing process. The overall applications range widely and have a significant influence, from tailored advice to improvements in healthcare for the future aspect. The text mining, natural language processing, picture analysis, link analysis, and other methods are the initial examples of web mining approaches for the system as well as users. While the data mining process is used with proper structured and semi-structured data, web mining mostly works with the unique unstructured web data.

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Web mining: a survey of current research, techniques, and software.

• QINGYU ZHANG  and 
• RICHARD S. SEGALL

Department of Computer & Information Technology, Arkansas State University, State University, Arkansas 72467-0130, USA

Corresponding author.

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The purpose of this paper is to provide a more current evaluation and update of web mining research and techniques available. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. For each tabulated research work, we examine such key issues as web mining process, methods/techniques, applications, data sources, and software used. Unlike previous investigators, we divide web mining processes into the following five subtasks: (1) resource finding and retrieving, (2) information selection and preprocessing, (3) patterns analysis and recognition, (4) validation and interpretation, and (5) visualization. This paper also reports the comparisons and summaries of selected software for web mining. The web mining software selected for discussion and comparison in this paper are SPSS Clementine, Megaputer PolyAnalyst, ClickTracks by web analytics, and QL2 by QL2 Software Inc. Applications of these selected web mining software to available data sets are discussed together with abundant presentations of screen shots, as well as conclusions and future directions of the research.

• web content mining
• web usage mining
• web structure mining
• web mining software
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• A Genetics Clustering-Based Approach for Weblog Data Cleaning Amine Ganibardi and Cherif Arab Ali 1 Oct 2018
• Web Usage Data Cleaning Amine Ganibardi and Chérif Arab Ali 8 August 2018
• A survey paper on techniques and applications of web usage mining Subhi Jain, Ruchira Rawat and Bina Bhandari 1 Nov 2017
• Automating the Extraction of Static Content and Dynamic Behaviour from e-Commerce Websites Joaõ Pedro Dias and Hugo Sereno Ferreira 1 Jan 2017 | Procedia Computer Science, Vol. 109
• Analysis of Users’ Behavior in Structured e-Commerce Websites Sergio Hernandez, Pedro Alvarez, Javier Fabra and Joaquin Ezpeleta 1 Jan 2017 | IEEE Access, Vol. 5
• Contribution to ontologies building using the semantic web and web mining Mohamed El Asikri, Jalal Laassiri, Salah-ddine Krit and Hassan Chaib 1 Sep 2016
• A comprehensive study on the effects of using data mining techniques to predict tie strength Mohammad Karim Sohrabi and Soodeh Akbari 1 Jul 2016 | Computers in Human Behavior, Vol. 60
• Classifier and feature set ensembles for web page classification Aytuğ Onan 29 June 2015 | Journal of Information Science, Vol. 42, No. 2
• Application and Significance of Web Usage Mining in the 21st Century: A Literature Review M. Aldekhail 1 February 2016 | International Journal of Computer Theory and Engineering, Vol. 8, No. 1
• An Automated Approach for Requirements Specification Maintenance J. Esparteiro Garcia and Ana C. R. Paiva 2 March 2016
• Social Media Mining Vipul Gupta and Mayank Gupta 1 Jan 2016
• Real-time web mining application to support decision-making process Jan Hovad, Martin Lnenicka and Jitka Komarkova 1 Jul 2015
• Artificial Immune System Based Web Page Classification Aytuğ Onan 1 Jan 2015
• A Proposal of a Big Web Data Application and Archive for the Distributed Data Processing with Apache Hadoop Martin Lnenicka, Jan Hovad and Jitka Komarkova 24 October 2015
• Willingness to Be Financially Informed and the Benefits of Nudging Investors to Do So Remo Sttssel 1 Jan 2015 | SSRN Electronic Journal, Vol. 2
• Webometrics benefitting from web mining? An investigation of methods and applications of two research fields David Gunnarsson Lorentzen 9 January 2014 | Scientometrics, Vol. 99, No. 2
• Hsin-Chang Yang  and 
• Chung-Hong Lee
• Parallel data mining techniques on Graphics Processing Unit with Compute Unified Device Architecture (CUDA) Liheng Jian, Cheng Wang, Ying Liu, Shenshen Liang and Weidong Yi et al. 26 August 2011 | The Journal of Supercomputing, Vol. 64, No. 3
• A Comprehensive Survey on Web Content Extraction Algorithms and Techniques Sumaia Mohammed Al-Ghuribi and Saleh Alshomrani 1 Jun 2013
• RASIM M. ALGULIEV , 
• RAMIZ M. ALIGULIYEV , and 
• NIJAT R. ISAZADE
• A devised framework for content recommendation system using collaborative log mining C. R. Varnagar, N. N. Madhak, T. M. Kodinariya and R. S. Agrawal 1 Mar 2013
• Web usage mining: A review on process, methods and techniques C. R. Varnagar, N. N. Madhak, T. M. Kodinariya and J. N. Rathod 1 Feb 2013
• Web data mining trends and techniques Ujwala Manoj Patil and J. B. Patil 3 August 2012
• Multiple factor hierarchical clustering algorithm for large scale web page and search engine clickstream data Gang Kou and Chunwei Lou 14 February 2010 | Annals of Operations Research, Vol. 197, No. 1
• Research on mobile learning system based on Web mining Zhengqiao Xu and Dewei Zhao 1 Jul 2012
• External semantic annotation of web-databases Benjamin Donz and Dietmar Bruckner 1 May 2012
• Web Mining and Security in E-commerce Shaikh Mohammed Atiq, Dayanand Ingle and B. B. Meshram 1 Jan 2012
• XIANYONG FANG , 
• CHRISTIAN JACQUEMIN , and 
• FRÉDÉRIC VERNIER
• Web Data Mining research: A survey Brijendra Singh and Hemant Kumar Singh 1 Dec 2010
• JUNLIN ZHOU , 
• ALEKSANDAR LAZAREVIC , 
• KUO-WEI HSU , 
• JAIDEEP SRIVASTAVA , 
• YAN FU , and 
• LAN ZHAO  and 
• JOSÉ BORGES  and 
• MARK LEVENE
• UNIL YUN  and 
• KEUN HO RYU
• LI-AN HO , 
• TSUNG-HSIEN KUO , 
• CHINHO LIN , and 
• BINSHAN LIN
• Review of data, text and web mining software Qingyu Zhang and Richard S. Segall 4 May 2010 | Kybernetes, Vol. 39, No. 4
• ANTONIO CABALLERO-LUQUE , 
• PABLO ARAGONÉS-BELTRÁN , 
• MÓNICA GARCÍA-MELÓN , and 
• CARLOS DEMA-PÉREZ
• Credit card customer analysis based on panel data clustering Guangli Nie, Yibing Chen, Lingling Zhang and Yuhong Guo 1 May 2010 | Procedia Computer Science, Vol. 1, No. 1
• Commercial Data Mining Software Qingyu Zhang and Richard S. Segall 7 July 2010
• Maximal Sequential Patterns René Arnulfo García-Hernández, J. Fco. Martínez-Trinidad and J. Ariel Carrasco-Ochoa

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Data Mining Case Studies & Benefits

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• Key Takeaways

Data mining has improved the decision-making process for over 80% of companies. (Source: Gartner).

Statista reports that global spending on robotic process automation (RPA) is projected to reach $98 billion by 2024, indicating a significant investment in automation technologies.

According to Grand View Research, the global data mining market will reach $16,9 billion in 2027.

Ethical Data Mining preserves individual rights and fosters trust.

A successful implementation requires defining clear goals, choosing data wisely, and constant adaptation.

Data mining case studies help businesses explore data for smart decision-making. It’s about finding valuable insights from big datasets. This is crucial for businesses in all industries as data guides strategic planning. By spotting patterns in data, businesses gain intelligence to innovate and stay competitive. Real examples show how data mining improves marketing and healthcare. Data mining isn’t just about analyzing data; it’s about using it wisely for meaningful changes.

The Importance of Data Mining for Modern Business:

Data mining has taken on a central role in the modern world of business. Data is a major issue for businesses today. Making informed decisions with this data can be crucial to staying competitive. This article explores the many aspects of data mining and its impact on decisions.

• Unraveling Data Landscape

Businesses generate a staggering amount of data, including customer interactions, market patterns, and internal operations. Decision-makers face an information overload without effective tools for sorting through all this data.

Data mining is a process which not only organizes, structures and extracts patterns and insights from this vast amount of data. It acts as a compass to guide decision makers through the complex landscape of data.

• Empowering Strategic Decision Making

Data mining is a powerful tool for strategic decision making. Businesses can predict future trends and market behavior by analyzing historical data. This insight allows businesses to better align their strategies with predicted shifts.

Data mining can provide the strategic insights required for successful decision making, whether it is launching a product, optimizing supply chain, or adjusting pricing strategies.

• Customer-Centric Determining

Understanding and meeting the needs of customers is paramount in an era where customer-centricity reigns. Data mining is crucial in determining customer preferences, behaviors, and feedback.

This information allows businesses to customize products and services in order to meet the expectations of customers, increase satisfaction and build lasting relationships. With customer-centric insights, decision-makers can make choices that resonate with their target audiences and foster loyalty and brand advocacy.

Data Mining: Applications across industries

Data mining is transforming the way companies operate and make business decisions. This article explores the various applications of data-mining, highlighting case studies that illuminate its impact in the healthcare, retail, and finance sectors.

• Healthcare Case Studies:

Data mining is a powerful tool in the healthcare industry. It can improve patient outcomes and treatment plans. Discover compelling case studies in which data mining played a crucial role in predicting patterns of disease, optimizing treatment and improving patient care. These examples, which range from early detection of health risks to personalized medicines, show the impact that data mining has had on the healthcare industry.

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• Retail Success stories:

Retail is at the forefront of leveraging data mining to enhance customer experiences and streamline operations. Discover success stories of how data mining empowered businesses to better understand consumer behavior, optimize their inventory management and create personalized marketing strategies.

These case studies, which range from e-commerce giants and brick-and-mortar shops, show how data mining can boost sales, improve customer satisfaction, transform the retail landscape, etc.

• Financial Sector Examples:

Data mining is a valuable tool in the finance industry, where precision and risk assessment are key. Explore case studies that demonstrate how data mining can be used for fraud detection and risk assessment. These examples demonstrate how financial institutions use data mining to make better decisions, protect against fraud, and customize services to their clients’ needs.

• Data Mining and Education:

Data mining has been used in the education sector to enhance learning beyond healthcare, retail and finance. Learn how educational institutions use data mining to optimize learning outcomes, analyze student performance and personalize materials. These examples, ranging from adaptive learning platforms and predictive analytics to predictive modeling, demonstrate the potential for data mining to revolutionize how we approach education.

• Manufacturing efficiency:

Data mining is a powerful tool for streamlining manufacturing processes. Examine case studies that demonstrate how data mining can be used to improve supply chain management, predict maintenance requirements, and increase overall operational efficiency. These examples show how data-driven insights can lead to cost savings, increased productivity, and a competitive advantage in manufacturing.

Data mining is a key component in each of these applications. It unlocks insights, streamlines operations, and shapes the future of decisions. Data mining is transforming the landscapes of many industries, including healthcare, retail, education, finance, and manufacturing.

Data Mining Techniques

Data mining techniques help businesses gain an edge by extracting valuable insights and information from large datasets. This exploration will provide an overview of the most popular data mining methods, and back each one with insightful case studies.

• Popular Data Mining Techniques

Clustering Analysis

The clustering technique involves grouping data points based on a set of criteria. This method is useful for detecting patterns in data sets and can be used to segment customers, detect anomalies, or recognize patterns. The case studies will show how clustering can be used to improve marketing strategies, streamline products, and increase overall operational efficiency.

Association Rule Mining

Association rule mining reveals relationships between variables within large datasets. Market basket analysis is a common application of association rule mining, which identifies patterns in co-occurring products in transactions. Real-world examples of how association rule mining is used in retail to improve product placements, increase sales, and enhance the customer experience.

Decision Tree Analysis

The decision tree is a visual representation of the process of making decisions. This technique is a powerful tool for classification tasks. It helps businesses make decisions using a set of criteria. Through case studies, you will learn how decision tree analyses have been used in the healthcare industry for disease diagnosis and fraud detection, as well as predictive maintenance in manufacturing.

Regression Analysis

Regression analysis is a way to explore the relationship between variables. This allows businesses to predict and understand how one variable affects another. Discover case studies that demonstrate how regression analysis is used to predict customer behavior, forecast sales trends, and optimize pricing strategies.

Benefits and ROI:

Businesses are increasingly realizing the benefits of data mining in the current dynamic environment. The benefits are numerous and tangible, ranging from improved decision-making to increased operational efficiency. We’ll explore these benefits, and how businesses can leverage data mining to achieve significant gains.

• Enhancing Decision Making

Data mining provides businesses with actionable insight derived from massive datasets. Analyzing patterns and trends allows organizations to make more informed decisions. This reduces uncertainty and increases the chances of success. There are many case studies that show how data mining has transformed the decision-making process of businesses in various sectors.

• Operational Efficiency

Data mining is essential to achieving efficiency, which is the cornerstone of any successful business. Organizations can improve their efficiency by optimizing processes, identifying bottlenecks, and streamlining operations. These real-world examples show how businesses have made remarkable improvements in their operations, leading to savings and resource optimization.

• Personalized Customer Experiences

Data mining has the ability to customize experiences for customers. Businesses can increase customer satisfaction and loyalty by analyzing the behavior and preferences of their customers. Discover case studies that show how data mining has been used to create engaging and personalized customer journeys.

• Competitive Advantage

Gaining a competitive advantage is essential in today’s highly competitive environment. Data mining gives businesses insights into the market, competitor strategies, and customer expectations. These insights can give organizations a competitive edge and help them achieve success. Look at case studies that show how companies have outperformed their competitors by using data mining.

Calculating ROI and Benefits

To justify investments, businesses must also quantify their return on investment. Calculating ROI for data mining initiatives requires a thorough analysis of the costs, benefits, and long-term impacts. Let’s examine the complexities of ROI within the context of data-mining.

• Cost-Benefit Analysis

Prior to focusing on ROI, companies must perform a cost-benefit assessment of their data mining projects. It involves comparing the costs associated with implementing data-mining tools, training staff, and maintaining infrastructure to the benefits anticipated, such as higher revenue, cost savings and better decision-making. Case studies from real-world situations provide insight into cost-benefit analysis.

• Quantifying Tangible and intangible benefits

Data mining initiatives can yield tangible and intangible benefits. Quantifying tangible benefits such as an increase in sales or a reduction in operational costs is easier. Intangible benefits such as improved brand reputation or customer satisfaction are also important, but they may require a nuanced measurement approach. Examine case studies that quantify both types.

• Long-term Impact Assessment

ROI calculations should not be restricted to immediate gains. Businesses need to assess the impact their data mining projects will have in the future. Consider factors like sustainability, scalability, and ongoing benefits. Case studies that demonstrate the success of data-mining strategies over time can provide valuable insight into long-term impact assessment.

• Key Performance Indicators for ROI

Businesses must establish KPIs that are aligned with their goals in order to measure ROI. KPIs can be used to evaluate the success of data-mining initiatives, whether it is tracking sales growth, customer satisfaction rates, or operational efficiency. Explore case studies to learn how to select and monitor KPIs strategically for ROI measurement.

Data Mining Ethics

Data mining is a field where ethical considerations are crucial to ensuring transparent and responsible practices. It is important to carefully navigate the ethical landscape as organizations use data to extract valuable insights. This section examines ethical issues in data mining and highlights cases that demonstrate ethical practices.

• Understanding Ethical Considerations

Data mining ethics revolves around privacy, consent, and responsible information use. Businesses are faced with the question of how they use and collect data. Ethics also includes the biases in data and the fairness of algorithms.

• Balance Innovation and Privacy

Finding the right balance between privacy and innovation is a major ethical issue in data mining. In order to gain an edge in the market through data insights and to innovate, organizations must walk a tightrope between innovation and privacy. Case studies will illuminate how companies have successfully balanced innovation and privacy.

• Transparency and informed consent

Transparency in the processes is another important aspect of ethical data mining. This is to ensure that individuals are informed and consented before their data is used. This subtopic will explore the importance of transparency in data collection and processing, with case studies that highlight instances where organizations have established exemplary standards to obtain informed consent.

Exploring Data Mining Ethics is crucial as data usage evolves. Businesses must balance innovation, privacy, and transparency while gaining informed consent. Real-world cases show how ethical data mining protects privacy and builds trust.

Implementing Data Mining is complex yet rewarding. This guide helps set goals, choose data sources, and use algorithms effectively. Challenges like data security and resistance to change are common but manageable.

Considering ethics while implementing data mining shows responsibility and opens new opportunities. Organizations prioritizing ethical practices become industry leaders, mitigating risks and achieving positive impacts on business, society, and technology. Ethics and implementation synergize in data mining, unlocking its true potential.

• Q. What ethical considerations are important in data mining?

Privacy and consent are important ethical considerations for data mining.

• Q. How can companies avoid common pitfalls when implementing data mining?

By ensuring the security of data, addressing cultural opposition, and encouraging continuous learning and adaptation.

• Q. Why is transparency important in data mining?

Transparency and consent to use collected data ethically are key elements of building trust.

• Q. What are the main steps to implement data mining in businesses?

Define your objectives, select data sources, select algorithms and monitor continuously.

• Q. How can successful organizations use data mining to gain a strategic advantage?

By taking informed decisions, improving operations and staying on top of the competition.

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Text Analytics: 5 Examples To Open Your Eyes to Your Own Opportunities

Advanced Analytics

Across all industries, business users are learning the value of their raw text. By mining this data, they can save operational costs, uncover relationships previously not available, and gain insights into future trends.

Is it hard to believe that 80 percent of business data is in the form of text?

Examples include call center transcripts, online reviews, customer surveys, and other text documents. This untapped text data is a gold mine waiting to be discovered. Text mining and analytics turn these untapped data sources from words to actions. However, to do so, each company needs to have the skillsets, infrastructure, and analytic mindset to adopt these cutting edge technologies. To better assess your ability to embrace text mining solutions, Zencos has developed a self-evaluation analytics checklist to assess your companies readiness to become analytically driven.

What is Text Mining?

Data scientists analyze text using advanced data science techniques. The data from the text reveals customer sentiments toward subjects or unearths other insights.

There are two ways to use text analytics (also called text mining) or natural language processing (NLP) technology.

• The first method is analyzing text that exists, such as customer reviews, gleaning valuable insights.
• The second method is to structure your text so that it can be used in machine learning models to predict future events.

You can convert free-form text into structured data for use in predictive models or unearth hidden patterns in your data. With text mining, you can flag potential customers eligible for cross-selling, forecast customers’ sentiments , or understand behaviors that predict fraud .

How is Text Analytics Used in Business?

There are so many ground-breaking ways that companies can integrate previously untapped data sources and NLP into their operations.

Here are a few examples that our data science staff suggested:

The good news is that many companies are already using text to drive business operations successfully. When moving a data strategy from business intelligence reporting into data science – text analytics can be a way for you to optimize your processes.

Let’s look at a few examples that may highlight a chance for your company to implement text analytics.

Market Research: Find What Consumers Value Most

There are plenty of stats that can tell you consumers are interested in other’s opinions and experiences. These statistics reveal that at least 90% of us are influenced by what we read. Even more so if it’s a negative review – the sentiment resonates. In recent years, multiple sites have collected reviews for local eateries, vacation destinations, and, of course, consumer products.

A positive review is a form of social proof.

If your company is considering entering a new market or needs to research product ideas, why not start with online reviews from real users? This was the very idea that drove a major Amazon case study on pricing by a young analyst team.

They wanted to understand the best speakers available to purchase at the $150 price point. They theorized that if you’re going to develop and market a new product, it is useful to understand what features are most valued. What a great business use case and a reasonable example of analytics!

The team extracted data for five speakers based on popular brands that Amazon customers had reviewed. The data contained consumer ranking, price, and all customer reviews.

This data was a mix of structured data (ratings, price) and unstructured data (review text).

Using the customer rating, these junior data scientists wanted to learn which product characteristics influenced scores. The following figure shows the products with the final text topic extraction analysis.

Datamation content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More .

Companies understand that data mining can provide insights to improve the organization. Yet, many struggle with the right types of data to collect, where to start, or what project may benefit from data mining.

Examining the data mining success of others in a variety of circumstances illuminates how certain methods and software in the market can assist companies. See below how five organizations benefited from data mining in different industries: cybersecurity, finance, health care, logistics, and media.

See more: What is Data Mining? Types & Examples

1. Cerner Corporation

Over 14,000 hospitals, physician’s offices, and other medical facilities use Cerner Corporation’s software solutions.

Cerner’s access allows them to combine patient medical records and medical device data to create an integrated medical database and improve health care.

Using Cloudera’s data mining allows different devices to feed into a common database and predict medical conditions.

“In our first attempts to build this common platform, we immediately ran into roadblocks,” says Ryan Brush, senior director and distinguished engineer at Cerner.

“Our clients are reporting that the new system has actually saved hundreds of lives by being able to predict if a patient is septic more effectively than they could before.”

Industry: Health care

Data mining provider: Cloudera

• Collect data from unlimited and different sources
• Enhance operational and financial performance for health care facilities
• Improve patient diagnosis and save lives

Read the Cerner Corporation and Cloudera, Inc. case study.

DHL Temperature Management Solutions provides temperature controlled pharmaceutical logistics to ensure pharmaceutical and biological goods stay within required temperature ranges to retain potency.

Previously, DHL transferred data into spreadsheets that took a week to compile and would only contain a portion of the potential information.

Moving to DOMO’s data mining platform allows for real-time reporting of a broader set of data categories to improve insight.

“We’re able to pinpoint issues that we couldn’t see before. For example, a certain product, on a certain lane, at a certain station is experiencing an issue repeatedly,” says Dina Bunn, global head of central operations and IT for DHL Temperature Management Solutions.

Industry: Logistics

Data mining provider: DOMO

• Real-time versus week-old logistics information
• More insight into sources of delays or problems at both a high and a detailed level
• More customer engagement

Read the DHL and DOMO case study.

See more: Current Trends & Future Scope of Data Mining

The Nasdaq electronic stock exchange integrates Sisense’s data mining capabilities into their IR Insight software to help customers analyze huge data sets.

“Our customers rely on a range of content sets, including information that they license from others, as well as data that they input themselves,” says James Tickner, head of data analytics for Nasdaq Corporate Solutions.

“Being able to layer those together and attain a new level of value from content that they’ve been looking at for years but in another context.”

The combined application provides real-time analysis and clear reports easy for customers to understand and communicate internally.

Industry: Finance

Data mining provider: Sisense

• Meets rigorous data security regulations
• Quickly processes huge data sets from a variety of sources
• Provides clients with new ways to visualize and interpret data to extract new value

Read or watch the Nasdaq and Sisense case study.

The Public Broadcasting System (PBS) of the U.S. manages an online website to service 353 PBS member stations and their viewers. Their 330 million sessions, 800 million page views, and 17.5 million episode plays generate enormous data that the PBS team struggled to analyze.

PBS worked with LunaMetrics to perform data mining on the Google Analytics 360 platform to speed up insights into PBS customers.

Dan Haggerty, director of digital analytics for PBS, says “that was the coolest thing about it. A machine took our data without prior assumptions and reaffirmed and strengthened ideas that subject matter experts already suspected about our audiences based on our contextual knowledge.”

Industry: Media

Data mining provider: Google Analytics and LunaMetrics

• Identified seven key audience segments based on web behaviors
• Developed in-depth personas per segment through data mining
• Insights help direct future content and feature development

Read the PBS, LunaMetrics, and Google Analytics case study.

5. The Pegasus Group

Cyber attackers compromised and targeted the data mining system (DMS) of a major network client of The Pegasus Group and launched a distributed denial-of-service (DDoS) attack against 1,500 services.

Under extreme time pressure, The Pegasus Group needed to find a way to use data mining to analyze up to 35GB of data with no prior knowledge of the data contents.

“[I analyzed] the first three million lines and [used RapidMiner’s data mining to perform] a stratified sampling to see which ones [were] benign, which packets [were] really part of the network, and which packets were part of the attack,” says Rodrigo Fuentealba Cartes of The Pegasus Group.

“In just 15 minutes … I used this amazing simulator to see what kinds of parameters I could use to filter packets … and in another two hours, the attack was stopped.”

Industry: Cybersecurity

Data mining provider: RapidMinder

• Uploaded and analyzed three million lines of data 
• Recommended analysis models provided answers within 15 minutes
• Data analysis suggested solutions that stopped the attack within two hours

Watch The Pegasus Group and RapidMiner case study.

See more: Top Data Mining Tools

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Mining Customer Behavior in Trial Period of a Web Application Usage—Case Study

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• First Online: 21 April 2016
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• Goran Matošević 7 &
• Vanja Bevanda 7  

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 464))

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This paper proposes models for predicting customer conversion from trial account to full paid account of web application. Two models are proposed with focus on content of the application and time. In order to make a customer’s behavior prediction, data is extracted from web application’s usage log in trial period and processed with data mining techniques. For both models, content and time based, the same selected classification algorithms are used: decision trees, Naïve Bayes, k-Nearest Neighbors and One Rule classification. Additionally, a cluster algorithm k-means is used to see if clustering by two clusters (for converted and not-converted users) can be formed and used for classification. Results showed high accuracy of classification algorithms in early stage of trial period which can serve as a basis for an identification of users that are likely to abandon the application and not convert.

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Matošević, G., Bevanda, V. (2016). Mining Customer Behavior in Trial Period of a Web Application Usage—Case Study. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Artificial Intelligence Perspectives in Intelligent Systems. Advances in Intelligent Systems and Computing, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-319-33625-1_30

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