A Hash-based Co-Clustering Algorithm for Categorical Data

TL;DR

This paper introduces a novel hash-based co-clustering algorithm for categorical data that efficiently finds meaningful clusters by leveraging Locality Sensitive Hashing, addressing challenges of feature importance and multiple cluster interpretations.

Contribution

The paper presents a new co-clustering method using Locality Sensitive Hashing to improve clustering quality and scalability for categorical data.

Findings

Capable of finding high-quality co-clusters across various datasets

Scales linearly with dataset size

Effective in handling feature importance and multiple cluster interpretations

Abstract

Many real-life data are described by categorical attributes without a pre-classification. A common data mining method used to extract information from this type of data is clustering. This method group together the samples from the data that are more similar than all other samples. But, categorical data pose a challenge when extracting information because: the calculation of two objects similarity is usually done by measuring the number of common features, but ignore a possible importance weighting; if the data may be divided differently according to different subsets of the features, the algorithm may find clusters with different meanings from each other, difficulting the post analysis. Data Co-Clustering of categorical data is the technique that tries to find subsets of samples that share a subset of features in common. By doing so, not only a sample may belong to more than one cluster but, the feature selection of each cluster describe its own characteristics. In this paper a novel Co-Clustering technique for categorical data is proposed by using Locality Sensitive Hashing technique in order to preprocess a list of Co-Clusters seeds based on a previous research. Results indicate this technique is capable of finding high quality Co-Clusters in many different categorical data sets and scales linearly with the data set size.