Application of inclined planes system optimization on data clustering

Data-mining is a branch of science which tends to extract a series of futures and some meaningful information from a huge database in proper time and cost. Clustering is one of the popular methods in this field. The purpose of clustering is to use a database and group together its items with similar characteristics. Application of clustering in many fields of science and engineering problems like Pattern recognition, data retrieval, bio-informatics, machine learning and the Internet cause to have significantly developed in the last decades. A rapid growth in the volume of information in databases revealed weakness of traditional methods like K-means in facing with huge data. In this paper a new clustering method based on the Inclined Planes system Optimization algorithm was proposed and evaluate on a series of standard datasets. Comparison study revealed a significant superiority over other similar clustering algorithms.