A Thorough Investigation of Link-Based Cluster Ensemble Approach for Data Clustering

Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., Euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than points in different partitions. The problem of clustering becomes more challenging when the data is categorical, that is, when there is no inherent distance measure between data values. Various clustering algorithms are developed to cluster or categorize the datasets. Many algorithms are used to cluster the categorical data. Some algorithms cannot be directly applied for clustering of categorical data. Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clustering’s from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clustering’s. This paper presents an overview of clustering ensemble methods that can be very useful for the categorical data clustering. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. Several attempts have been made to solve the problem of clustering categorical data via cluster ensembles. But these techniques generate a final data partition based on incomplete information. Secondly, we describe several clustering ensembles methods such as including Cluster Ensemble technique, Squared Error Adjacent Matrix algorithm, Hybrid Fuzzy Ensemble, and next explain their advantages, disadvantages and computational complexity. Finally, we compare the characteristics of clustering ensembles algorithms such as computational complexity, simplicity and accuracy on different datasets in previous techniques.