Machine learning techniques are increasingly popular tools for understanding complex biological data. Prior research has demonstrated the power of simple statistical clustering algorithms for disease class discovery and prediction. In this work we examine the efficacy of spectral and divisive clustering on gene expression microarray data. In particular we consider simultaneous expression cluste...

A common approach to clustering data is to view data objects as points in a metric space, and then to optimize a natural distance-based objective such as the k-median, k-means, or min-sum score. For applications such as clustering proteins by function or clustering images by subject, the implicit hope in taking this approach is that the optimal solution for the chosen objective will closely mat...

Clustering analysis often employs unsupervised learning techniques originally developed for vector quantization. In this framework, a frequent goal of clustering systems is to minimize the quantization error, which is aaected by many local minima. To avoid connnement of reference vectors to local minima of the quantization error and to avoid formation of dead units, hard c-means clustering algo...

some applications are critical and must designed fault tolerant system. usually voting algorithm is one of the principle elements of a fault tolerant system. two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. majority confronts with the problem of threshold limits and voter of weight...

MOTIVATION Hierarchical and relocation clustering (e.g. K-means and self-organizing maps) have been successful tools in the display and analysis of whole genome DNA microarray expression data. However, the results of hierarchical clustering are sensitive to outliers, and most relocation methods give results which are dependent on the initialization of the algorithm. Therefore, it is difficult t...

In many applications it is desirable to cluster high dimensional data along various subspaces, which we refer to as projective clustering. We propose a new objective function for projective clustering, taking into account the inherent trade-off between the dimension of a subspace and the induced clustering error. We then present an extension of the -means clustering algorithm for projective clu...

The customer load profile clustering method is used to make the TDLP (Typical Daily Load Profile) to estimate the quarter hourly load profile of non-AMR (Automatic Meter Reading) customers. This study examines how the repeated clustering method improves the ability to discriminate among the TDLPs of each cluster. The k-means algorithm is a well-known clustering technology in data mining. Repeat...

Clustering is a technique of grouping the similar items and dissimilar items so that the analysis of any data can be done efficiently and effectively. Although there are various clustering techniques implemented for the analysis of data but the clustering technique used here is based on fuzzy based clusters. Here in this paper an efficient clustering is proposed using fuzzy based SVM. The techn...