UNSUPERVISED DATA AND HISTOGRAM CLUSTERING USING INCLINED PLANES SYSTEM OPTIMIZATION ALGORITHM

Abstract

Within the last decades, clustering has gained significant recognition as one of the data mining methods, especially in the relatively new field of medical engineering for diagnosing cancer. Clustering is used as a database to automatically group items with similar characteristics. Researchers aim to introduce a novel and powerful algorithm known as Inclined Planes system Optimization (IPO), with capacity to overcome clustering problems. The proposed method identifies each agent used in the algorithm to indicate the centroids of the clusters and automatically select the number of centroids in each time interval (unsupervised clustering). The evaluation method for clustering is based on the Davies Bouldin index (DBi) to show cluster validity. Researchers compare known algorithm on series of data bases from various studies to demonstrate the power and capability of the proposed method. These datasets are popular for pattern recognition with diversity in space dimension. Method performance was tested on standard images as a dataset. Study results show significant method advantage over other algorithms.

Keywords

• Davies Bouldin index
• histogram
• image processing
• inclined planes system
• optimization
• soft computing
• unsupervised clustering