An Analysis of Leukemia Data Set for Various Clustering Algorithms

The data mining process is to extract information from large database, and it is non-trivial process of identifying valid, novel, potential useful and understandable pattern in data. The data mining task is using two major categories of predictive and descriptive tasks. Data mining involves the outlier detection, classification, clustering, regression and summarization. The clustering is the most important technique in data mining, which divides data into groups of similar object. Each groups ( cluster) consist of object that are similar among themselves. A wide range of clustering algorithms is available in literature and still an open area for researcher. In this paper Consider Affinity Propagation Clustering Algorithm and K-Means++ Clustering Algorithm. And I have tested two Kinds of Data set and the Experimental results shows Increase the Accuracy and Decrease the Execution Time