Improved Multi-objective Data Stream Clustering with Time and Memory Optimization

TL;DR

This paper presents IMOC-Stream, a novel multi-objective data stream clustering method that improves time and memory efficiency while accurately identifying arbitrarily shaped clusters in high-dimensional data streams.

Contribution

IMOC-Stream introduces a multi-objective framework with dual objectives, a new tree synopsis for memory reduction, and a genetic operation strategy to enhance clustering performance.

Findings

Outperforms existing stream clustering algorithms in NMI and ARAND metrics.

Effectively finds arbitrarily shaped, compact, and well-separated clusters.

Reduces computation time and memory usage in high-dimensional data streams.

Abstract

The analysis of data streams has received considerable attention over the past few decades due to sensors, social media, etc. It aims to recognize patterns in an unordered, infinite, and evolving stream of observations. Clustering this type of data requires some restrictions in time and memory. This paper introduces a new data stream clustering method (IMOC-Stream). This method, unlike the other clustering algorithms, uses two different objective functions to capture different aspects of the data. The goal of IMOC-Stream is to: 1) reduce computation time by using idle times to apply genetic operations and enhance the solution. 2) reduce memory allocation by introducing a new tree synopsis. 3) find arbitrarily shaped clusters by using a multi-objective framework. We conducted an experimental study with high dimensional stream datasets and compared them to well-known stream clustering techniques. The experiments show the ability of our method to partition the data stream in arbitrarily shaped, compact, and well-separated clusters while optimizing the time and memory. Our method also outperformed most of the stream algorithms in terms of NMI and ARAND measures.