There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyze this huge amount of data and extract useful information from it.
Extraction of information is not the only process we need to perform; data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation. Once all these processes are over, we would be able to use this information in many applications such as Fraud Detection, Market Analysis, Production Control, Science Exploration, etc.
What is Data Mining?
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data. The information or knowledge extracted so can be used for any of the following applications −
- Market Analysis
- Fraud Detection
- Customer Retention
- Production Control
- Science Exploration
Data Mining Applications
Data mining is highly useful in the following domains −
- Market Analysis and Management
- Corporate Analysis & Risk Management
- Fraud Detection
Apart from these, data mining can also be used in the areas of production control, customer retention, science exploration, sports, astrology, and Internet Web Surf-Aid
Market Analysis and Management
Listed below are the various fields of market where data mining is used −
- Customer Profiling − Data mining helps determine what kind of people buy what kind of products.
- Identifying Customer Requirements − Data mining helps in identifying the best products for different customers. It uses prediction to find the factors that may attract new customers.
- Cross Market Analysis − Data mining performs Association/correlations between product sales.
- Target Marketing − Data mining helps to find clusters of model customers who share the same characteristics such as interests, spending habits, income, etc.
- Determining Customer purchasing pattern − Data mining helps in determining customer purchasing pattern.
- Providing Summary Information − Data mining provides us various multidimensional summary reports.
Corporate Analysis and Risk Management
Data mining is used in the following fields of the Corporate Sector −
- Finance Planning and Asset Evaluation − It involves cash flow analysis and prediction, contingent claim analysis to evaluate assets.
- Resource Planning − It involves summarizing and comparing the resources and spending.
- Competition − It involves monitoring competitors and market directions.
Data mining is also used in the fields of credit card services and telecommunication to detect frauds. In fraud telephone calls, it helps to find the destination of the call, duration of the call, time of the day or week, etc. It also analyzes the patterns that deviate from expected norms.
Data mining deals with the kind of patterns that can be mined. On the basis of the kind of data to be mined, there are two categories of functions involved in Data Mining −
- Classification and Prediction
The descriptive function deals with the general properties of data in the database. Here is the list of descriptive functions −
- Class/Concept Description
- Mining of Frequent Patterns
- Mining of Associations
- Mining of Correlations
- Mining of Clusters
Class/Concept refers to the data to be associated with the classes or concepts. For example, in a company, the classes of items for sales include computer and printers, and concepts of customers include big spenders and budget spenders. Such descriptions of a class or a concept are called class/concept descriptions. These descriptions can be derived by the following two ways −
- Data Characterization − This refers to summarizing data of class under study. This class under study is called as Target Class.
- Data Discrimination − It refers to the mapping or classification of a class with some predefined group or class.
Mining of Frequent Patterns
Frequent patterns are those patterns that occur frequently in transactional data. Here is the list of kind of frequent patterns −
- Frequent Item Set − It refers to a set of items that frequently appear together, for example, milk and bread.
- Frequent Subsequence − A sequence of patterns that occur frequently such as purchasing a camera is followed by memory card.
- Frequent Sub Structure − Substructure refers to different structural forms, such as graphs, trees, or lattices, which may be combined with item-sets or subsequences.