The weighted fuzzy c-mean clustering algorithm (WFCM) and weighted fuzzy c-mean-adaptive cluster number (WFCM-AC) are extension of traditional fuzzy c-mean algorithm to stream data clustering algorithm. Clusters in WFCM are generated by renewing the centers of weighted cluster by iteration. On the other hand, WFCM-AC generates clusters by applying WFCM on the data & selecting best K± initialize...

Data mining in educational field is a major application of data mining, it use machine learning to learn from data by studying algorithms and their constructions. In Data mining, clustering is the task of grouping aset of objects in such a way that objects in the same group are more similar to each other than to those in other groups. There are too many algorithms for clustering technique but k...

Unsupervised learning methods such as clustering techniques are a natural choice for analyzing software quality by mining its related metrics. It is well known that clustering plays an important role in data mining tasks like in data analysis and information retrieval. In this paper, we have proposed an approach to cluster the pool of java classes based on the proximity between them. To know th...

Due to continuous growth of the internet technology, there is need to establish security mechanism. So for achieving this objective various NIDS has been propsed. Datamining is one of the most effective techniques used for intrusion detection. This work evaluates the performance of unsupervised learning techniques over benchmark intrusion detection datasets. The model generation is computation ...

Circuit partitioning plays a dominant role in VLSI physical design of chips. In this paper the newly proposed rank based k-medoid clustering algorithm is discussed, in order to partition the combinational circuit based on their interconnection distance among cell groups. Clustering analysis of the given circuit ,partition the set of objects into non overlapping subsets. The proposed ranked k-me...

In this text we propose a method which efficiently performs clustering of high-dimensional data. The method builds on random projection and the Kmeans algorithm. The idea is to apply K-means several times, increasing the dimensionality of the data after each convergence of K-means. We compare the proposed algorithm on four high-dimensional datasets, image, text and two synthetic, with K-means c...