Title :
A synchronization based algorithm for discovering ellipsoidal clusters in large datasets
Author :
Frigui, Hichem ; Rhouma, Mohamed Ben Hadj
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Memphis, TN, USA
Abstract :
This paper introduces a new scalable approach to clustering based on the synchronization of pulse-coupled oscillators. Each data point is represented by an integrate-and-fire oscillator and the interaction between oscillators is defined according to the relative similarity between the points. The set of oscillators self-organizes into stable phase-locked subgroups. Our approach proceeds by loading only a subset of the data and allowing it to self-organize. Groups of synchronized oscillators are then summarized and purged from memory. We show that our method is robust, scales linearly and can determine the number of clusters. The proposed approach is empirically evaluated with several synthetic data sets and is used to segment large color images
Keywords :
data mining; image colour analysis; image segmentation; oscillators; pattern clustering; scaling phenomena; self-adjusting systems; self-organising feature maps; stability; synchronisation; cluster number determination; data point representation; data subset loading; ellipsoidal cluster discovery; integrate-and-fire oscillators; large color image segmentation; large data sets; memory purging; oscillator interactions; pulse-coupled oscillator synchronization; relative point similarity; robust method; scalable clustering approach; self-organization; stable phase-locked subgroups; synchronization-based algorithm; synchronized oscillator group summarization; Clustering algorithms; Color; Data analysis; Data mining; Image segmentation; Mathematics; Multimedia databases; Oscillators; Partitioning algorithms; Robustness;
Conference_Titel :
Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
0-7695-1119-8
DOI :
10.1109/ICDM.2001.989511
