Leukemia is a hematologic cancer which develops in blood tissue and triggers rapid production of immature and abnormal shaped white blood cells. Based on statistics it is found that the leukemia is one of the leading causes of death in men and women alike. Microscopic examination of blood sample or bone marrow smear is the most effective technique for diagnosis of leukemia. Pathologists analyze...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an important problem in a wide variety of fields. Including data mining, pattern recognition, and bioinformatics. It aims to organize a collection of data items into...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Data mining is a computational intelligence discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decision making. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an impor...

In a typical content based image retrieval (CBIR) system, target images are sorted by feature similarities with respect to the query. These methods fail to capture similarities among target images and user feedback. To overcome this problem existing methods combine relevance feedback and clustering. But clustering requires more number of expensive distance calculations. To remedy this problem w...

Cluster analysis is a useful tool for data analysis. Clustering methods are used to partition a data set into clusters such that the data points in the same cluster are the most similar to each other and the data points in the different clusters are the most dissimilar. The mean shift was originally used as a kernel-type weighted mean procedure that had been proposed as a clustering algorithm. ...

Signed networks allow to model positive and negative relationships. We analyze existing extensions of spectral clustering to signed networks. It turns out that existing approaches do not recover the ground truth clustering in several situations where either the positive or the negative network structures contain no noise. Our analysis shows that these problems arise as existing approaches take ...

Suppose that a sample of people independently examine a fixed set of k items and then rank these items according to personal judgment. Whatever the nature of these items, each person produces a ranking. This paper aims at clustering people into different groups according to their preferences. We propose the exponential blurring mean-shift (EBMS) algorithm which shifts the rankings to new locati...

Clustering is one of the very important technique used for classification of large dataset and widely applied to many applications including analysis of social networking sites, aircraft accidental, company performance etc. In recent days, Communication, advertising through social networking sites are most popular and interactive strategy among the users. This research attempts to find the larg...

Data clustering is a process of organizing data into certain groups such that the objects in the one cluster are highly similar but dissimilar to the data objects in other clusters. K-means algorithm is one of the popular algorithms used for clustering but k-means algorithm have limitations like it is sensitive to noise ,outliers and also it does not provides global optimum results. To overcome...

Mean-Shift (MS) is a powerful non-parametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets. In this paper, for the purpose of algorithm speedup, we develop an agglomerative MS clustering method called Agglo-MS, along with its mode-seeking ability and convergence property analysis. Our method is built upon a...