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The analysis of data streams has received considerable attention over thepast few decades due to sensors, social media, etc. It aims to recognizepatterns 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). Thismethod, unlike the other clustering algorithms, uses two different objectivefunctions to capture different aspects of the data. The goal of IMOC-Stream isto: 1) reduce computation time by using idle times to apply genetic operationsand enhance the solution. 2) reduce memory allocation by introducing a new treesynopsis. 3) find arbitrarily shaped clusters by using a multi-objectiveframework. We conducted an experimental study with high dimensional streamdatasets and compared them to well-known stream clustering techniques. Theexperiments show the ability of our method to partition the data stream inarbitrarily shaped, compact, and well-separated clusters while optimizing thetime and memory. Our method also outperformed most of the stream algorithms interms of NMI and ARAND measures.

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