Clustering high dimensional non-linear data with denclue, optics and clique algorithms

Abstract

Clustering is a technique in data mining which deals with huge amount of data. Clustering is intended to help a user in discovering and understanding the natural structure in a data set and abstract the meaning of large dataset. It is the task of partitioning objects of a data set into distinct groups such that two objects from one cluster are similar to each other, whereas two objects from distinct clusters are dissimilar. Clustering is unsupervised learning in which we are not provided with classes, where we can place the data objects. With the advent growth of high dimensional data such as microarray gene expression data, and grouping high dimensional data into clusters will encounter the similarity between the objects in the full dimensional space is often invalid because it contains different types of data. The process of grouping into high dimensional data into clusters is not accurate and perhaps not up to the level of expectation when the dimension of the dataset is high.