Density-based clustering algorithms are widely used for discovering clusters in pattern recognition and machine learning. They can deal with non-hyperspherical robust to outliers. However, the runtime of density-based is heavily dominated by neighborhood finding density estimation which time-consuming. Meanwhile, traditional acceleration methods using indexing techniques such as KD-tree may not...

A relational dataset is often analyzed by optimally assigning a label to each element through clustering or ordering. While similar characterizations of would be achieved both and ordering methods, the former has been studied much more actively than latter, particularly for data represented as graphs. This study fills this gap investigating methodological relationships between several focusing ...

With the rapid growth of the World Wide Web and the capacity of digital data storage, tremendous amount of data are generated daily from business and engineering to the Internet and science. The Internet, financial realtime data, hyperspectral imagery, and DNA microarrays are just a few of the common sources that feed torrential streams of data into scientific and business databases worldwide. ...

Clustering is a fundamental problem in many scientific applications. Standard methods such as k-means, Gaussian mixture models, and hierarchical clustering, however, are beset by local minima, which are sometimes drastically suboptimal. Recently introduced convex relaxations of k-means and hierarchical clustering shrink cluster centroids toward one another and ensure a unique global minimizer. ...

One of the most important goals of unsupervised learning is to discover meaningful clusters in data. Clustering algorithms strive to discover groups, or clusters, of data points which belong together because they are in some way similar. The research presented in this thesis focuses on using Bayesian statistical techniques to cluster data. We take a model-based Bayesian approach to defining a c...

Analyzing the affinity matrix spectrum is an increasingly popular data clustering method. We propose three new algorithmic components which are appropriate for improving performance of spectral clustering. First, observing the eigenvectors suggests to use a K-lines algorithm instead of the commonly applied K-means. Second, the clustering works best if the affinity matrix has a clear block struc...

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The innumerable clustering methods which exist today form the basis of Data Mining and Cluster Analysis. This paper details the distinct classifications of clustering methods, describes prominent examples for each such classification and aims to bring about the comparison between the primary clustering techniques which form the basis of all the others, i.e. the Hierarchical and Partitional algo...

Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data in each subset according to some defined distanc...