Profit Pattern Mining Using Soft Computing for Decision Making: Pattern Mining Using Vague Set and Genetic Algorithm

Profit Pattern Mining Using Soft Computing for Decision Making: Pattern Mining Using Vague Set and Genetic Algorithm

Vivek Badhe, R. S. Thakur, G. S. Thakur
Copyright: © 2017 |Pages: 27
DOI: 10.4018/978-1-5225-0536-5.ch011
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Abstract

Problem of decision making is a crucial task in every business. Profit Pattern Mining hit the target by minimizes the gap between statistical based pattern generation and value base decision making. But this job is found very difficult when it depends on the large, imprecise and vague environment, which is frequent in recent years. The concept of soft computing with data mining is novel way to address this difficulty. The general approaches to association rule mining focus on inducting rule by using correlation among data and finding frequent occurring patterns. The major technique uses support and confidence measures for generating rules which is not adequate nowadays as a measure of interest, since the data have become more multifaceted these days, it's a necessary to find solution that deals with such problems and uses some new measures like profit, significance etc. In this chapter, authors apply concept of pattern mining with vague set theory, Genetic algorithm theory and related properties to the commercial management to deal with business decision making problem.
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Introduction

In last few decades, information is being generated at rapid pace. If Moore’s law be applicable in information generation, then we surely can say it will be a 100 or 1000 times faster than the normal chipset designing rate to new trends. Because of this huge amount of information, database systems have been and are being developed to manage such a pile. To store information is one thing, but to deal with it is another. To recognize and extract the hidden knowledge and potentially interesting patterns from these large databases is accomplished by Data Mining. We know that the association rule mining algorithm is capable to generate the thousands or even millions of rules, but all the rules generate by mining algorithms may not be interesting. As the rules generated by association rule mining algorithms have only statistical significance and alone not capable to give the interesting results. Thus require additional technique like genetic algorithm to discover the interesting rules.

Data Mining is a research area where large databases are processed and knowledge discovery is done by application of algorithms that have both statistical and logical significance. As these databases have information from various sources, they are liable to have some magnitude of uncertainty and vagueness in them. To administer with vagueness in databases, vague rule generation is a new direction in finding out the correlations and rules that eventually maximize the business profit as well as an inclination towards decision making process.

Data Mining

Data Mining (DM) Techniques (Pujari, 2001) is being highly used for extracting the hidden predictive information from large databases. data mining is also an interesting research area that raises several challenging problems. Profit Pattern Mining is one of them which is a new direction of data mining that deals with prime goal of any business that is to maximize profit.

Data mining (Han et al. 2001) may also be viewed as the process of turning the data into information, the information into action, and action into value or profit. The Figure 1 shows the typical major components of data mining system:

Figure 1.

Architecture of a typical data mining system

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Knowledge Discovery in Databases

Knowledge Discovery in Databases (KDD) (Han et al. 2001) refers to the nontrivial unknown and potentially useful information from data in databases. There is a series of steps/stages involved in KDD shown in Figure 2.

Figure 2.

Stages involved in knowledge discovery in database

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  • 1.

    Data Integration: It is a stage, where multiple data sources often heterogeneous, may be combined in a common source.

  • 2.

    Data Selection and Cleaning: It is a stage, concerned with selecting or segmenting the data that are relevant to some criteria and removes noise, irrelevant and incorrect data from the data collection.

  • 3.

    Data Transformation: This stage is also known as data consolidation, where data are transformed or consolidate into forms appropriate for mining by perfoffi1ing summary or aggregation operations, for instance.

  • 4.

    Data Mining/Pattern Discovery: An essential process where intelligent methods are applied in order to extract potential useful patterns.

  • 5.

    Interpretation / Pattern Evaluation: This stage identifies the truly intersecting patterns representing knowledge based on some interestingness measures and uses visualization technique to help, understand and intercept the data mining results to users.

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