Unsupervised Classification of Mixed Data Type of Attributes Using Genetic Algorithm (Numeric, Categorical, Ordinal, Binary, Ratio-Scaled)

Abstract

Data mining discloses hidden, previously unknown, and potentially useful information from large amounts of data. As comparison to the traditional statistical and machine learning data analysis techniques, data mining emphasizes to provide a convenient and complete environment for the data analysis. Data mining has become a popular technology in analyzing complex data. Clustering is one of the data mining core techniques. In the field of data mining and data clustering, it is a highly desirable task to perform cluster analysis on large data sets with mixed numeric, categorical, ordinal, and ratio-scaled with binary and nominal values. However, most already available data merging and grouping through clustering algorithms are effective for the numeric data rather than the mixed data set. For this purpose, this paper makes efforts to present a new amalgamation algorithm for these mixed data sets by modifying the common cost function, trace of the within cluster dispersion matrix. The genetic algorithm (GA) is used to optimize the new cost function to obtain valid clustering result. We can compare and analyze that the GA-based clustering algorithm is feasible for the high-dimensional data sets with mixed data values that are obtained in real life results. Core Idea of Our Paper: By this paper, we try to describe a technique for estimating the cost function metrics from mixed numeric, categorical and other type databases by using an uncertain grade-of-membership clustering model with the efficiency of Genetic Algorithm. This technique can be applied to the problem of opportunity analysis for business decision-making. This general approach could be adapted to many other applications where a decision agent needs to assess the value of items from a set of opportunities with respect to a reference set representing its business. For processing numeric attributes, instead of generalizing them, a prototype may be developed for experiments with synthetic and real data sets, and comparison with those of the traditional approaches. The results confirmed the feasibility of the framework and the superiority of the extended techniques.