Clustering of Heterogeneously Typed Data with Soft Computing - A Case Study

Abstract

Abstract. The problem of finding clusters in arbitrary sets of data has been attempted using different approaches. In most cases, the use of metrics in order to determine the adequateness of the said clusters is assumed. That is, the criteria yielding a measure of quality of the clusters depends on the distance between the elements of each cluster. Typically, one considers a cluster to be adequately characterized if the elements within a cluster are close to one another while, simultaneously, they appear to be far from those of different clusters. This intuitive approach fails if the variables of the elements of a cluster are not amenable to distance measurements, i.e., if the vectors of such elements cannot be quantified. This case arises frequently in real world applications where several variables (if not most of them) correspond to categories. The usual tendency is to assign arbitrary numbers to every category: to encode the categories. This, however, may result in spurious patterns: relationships between the variables which are not really there at the offset. It is evident that there is no truly valid assignment which may ensure a universally valid numerical value to this kind of variables. But there is a strategy which guarantees that the encoding will, in general, not bias the results. In this paper we explore such strategy. We discuss the theoretical foundations of our approach and prove that this is the best strategy in terms of the statistical behavior of the sampled data. We also show that, when applied to a complex real world problem, it allows us to generalize soft computing methods to find the number and characteristics of a set of clusters. We contrast the characteristics of the clusters gotten from the automated method with those of the experts.

Survey of State-of-the-Art Mixed Data Clustering Algorithms

“…Kuri-Morales et al [32] propose a strategy for the assignment of a numeric value to a categorical value. First, a mixed dataset is converted into a pure numeric dataset and then fuzzy C-means clustering algorithm is used.…”

“…Storlie et al [176] develop modelbased clustering for mixed datasets with missing feature values to cluster autism spectrum disorder. Researchers have used various types of clustering approaches for mixed data for heart disease [6], [16], [41], [78], occupational Medicine [57], [177], digital mammograms [178], acute inflammations [31], [65], [97], age of abalone snails [97], human life span [179], dermatology [80], medical diagnosis [98], toxicogenomics [180], genetic Regulation, analysis of biomedical datasets, [53] and cancer Samples Grouping [181].…”

Survey of state-of-the-art mixed data clustering algorithms

“…Ji et al [31] propose a fuzzy clustering method for mixed data by combining the similarity measure proposed by Ahmad and Dey [6] with the cluster center definition suggested by El-Sonbaty and Ismail [111]. Kuri-Moraleset al [32] propose a strategy for the assignment of a numeric value to a categorical value. First, a mixed dataset is converted into a pure numeric dataset and then fuzzy C-means clustering algorithm is used.…”

“…[176] develop model-based clustering for mixed datasets with missing feature values to cluster autism spectrum disorder. Researchers have used various types of clustering approaches for mixed data for heart disease [6], [16], [41], [78], occupational Medicine [57], [177], digital mammograms [178], acute inflammations [31], [65], [97], age of abalone snails [97], human life span [179], dermatology [80], medical diagnosis [98], toxicogenomics [180], genetic Regulation, analysis of bio-medical datasets, [53] and cancer Samples Grouping [181] b: Business and Marketing Hennig and Liao [7] apply mixed data clustering techniques for socio-economic stratification by using 2007 US survey data of consumer finances. Kassi et al [182] develop a mixed data clustering algorithm to segment gasoline services stations in Morocco to determine important features that can influence the profit of these service stations.…”