Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning

2024-07-23

IEEE International Conference on Distributed Computing Systems

AI Summary

Categorical data pose significant clustering challenges due to the absence of a well-defined distance metric, particularly when they exhibit multi-granular nested cluster structures. To address this, this work proposes a Multi-Granular Competitive Penalty Learning (MGCPL) mechanism that adaptively refines cluster structures in stages, integrated with a Cluster Aggregation and Metric Embedding (CAME) strategy based on learned distributions to enable robust clustering in the embedding space. This approach is the first to incorporate multi-granular competitive learning into categorical data modeling, offering both automatic granularity discovery and linear time complexity, thereby supporting scalability to large-scale datasets and compatibility with distributed pre-partitioning. Extensive experiments on multiple real-world datasets demonstrate its significant superiority over existing methods.

Technology Category

• Machine Learning: Clustering
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Data set composed of categorical features is very common in big data analysis tasks. Since categorical features are usually with a limited number of qualitative possible values, the nested granular cluster effect is prevalent in the implicit discrete distance space of categorical data. That is, data objects frequently overlap in space or subspace to form small compact clusters, and similar small clusters often form larger clusters. However, the distance space cannot be well-defined like the Euclidean distance due to the qualitative categorical data values, which brings great challenges to the cluster analysis of categorical data. In view of this, we design a Multi-Granular Competitive Penalization Learning (MGCPL) algorithm to allow potential clusters to interactively tune themselves and converge in stages with different numbers of naturally compact clusters. To leverage MGCPL, we also propose a Cluster Aggregation strategy based on MGCPL Encoding (CAME) to first encode the data objects according to the learned multi-granular distributions, and then perform final clustering on the embeddings. It turns out that the proposed MGCPL-guided Categorical Data Clustering (MCDC) approach is competent in automatically exploring the nested distribution of multi-granular clusters and highly robust to categorical data sets from various domains. Benefiting from its linear time complexity, MCDC is scalable to large-scale data sets and promising in pre-partitioning data sets or compute nodes for boosting distributed computing. Extensive experiments with statistical evidence demonstrate its superiority compared to state-of-the-art counterparts on various real public data sets.

Problem

• categorical data clustering
• multi-granular clusters
• nested cluster structure
• distance metric
• cluster analysis

Innovation

• Multi-Granular Competitive Learning
• Categorical Data Clustering
• Nested Cluster Structure
• Linear Time Complexity
• Cluster Aggregation