Data Mining

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Data Mining

Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information – information that can be used to increase revenue, cuts costs, or both. 

Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. 

Continuous Innovation 

Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost. 

Data, Information, and Knowledge Data

Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes: 

  • operational or transactional data such as, sales, cost, inventory, payroll, and accounting
  • nonoperational data, such as industry sales, forecast data, and macro economic data 
  • meta data – data about the data itself, such as logical database design or data dictionary definitions 


The patterns, associations, or relationships among all this data can provide information. For example, analysis of retail point of sale transaction data can yield information on which products are selling and when. 


Information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to provide knowledge of consumer buying behavior. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts. 

Data Warehouses 

Dramatic advances in data capture, processing power, data transmission, and storage capabilities are enabling organizations to integrate their various databases into data warehouses. Data warehousing is defined as a process of centralized data management and retrieval. Data warehousing, like data mining, is a relatively new term although the concept itself has been around for years. Data warehousing represents an ideal vision of maintaining a central repository of all organizational data. Centralization of data is needed to maximize user access and analysis. Dramatic technological advances are making this vision a reality for many companies. And, equally dramatic advances in data analysis software are allowing users to access this data freely. The data analysis software is what supports data mining. 

What can data mining do? 

Data mining is primarily used today by companies with a strong consumer focus – retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among “internal” factors such as price, product positioning, or staff skills, and “external” factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to “drill down” into summary information to view detail transactional data. 

With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual’s purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments. is pioneering massive data mining to transform its supplier relationships. captures point-of-sale transactions from over 2,000 stores in Africa and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse. allows more than 3,000 suppliers, to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. 

How does data mining work? 

While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought: 

  • Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.

  • Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities. 

  • Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining. 

  • Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer’s purchase of sleeping bags and hiking shoes. 

Data mining consists of five major elements: 

  • Extract, transform, and load transaction data onto the data warehouse system. 

  • Store and manage the data in a multidimensional database system. 

  • Provide data access to business analysts and information technology professionals. 

  • Analyze the data by application software. 

  • Present the data in a useful format, such as a graph or table. 

Different levels of analysis are available: 

  • Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
  • Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution. 
  • Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) . CART and CHAID are decision tree techniques used for classification of a dataset. They provide a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given outcome. CART segments a dataset by creating 2-way splits while CHAID segments using chi square tests to create multi-way splits. CART typically requires less data preparation than CHAID. 
  • Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the k-nearest neighbor technique.
  • Rule induction: The extraction of useful if-then rules from data based on statistical significance. 
  • Data visualization: The visual interpretation of complex relationships in multidimensional data. Graphics tools are used to illustrate data relationships.

What technological infrastructure is required?

Today, data mining applications are available on all size systems for mainframe, client/server, and PC platforms. There are two critical technological drivers: 

  • Size of the database: the more data being processed and maintained, the more powerful the system required. 
  • Query complexity: the more complex the queries and the greater the number of queries being processed, the more powerful the system required. 

Relational database storage and management technology is adequate for many data mining applications less than 50 gigabytes. However, this infrastructure needs to be significantly enhanced to support larger applications. Some vendors have added extensive indexing capabilities to improve query performance. Others use new hardware architectures such as Massively Parallel Processors (MPP) to achieve order-of-magnitude improvements in query time. For example, MPP systems from NCR link hundreds of high-speed Pentium processors to achieve performance levels exceeding those of the largest supercomputers

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