We present a novel anytime version of partitional clustering algorithm, such as k-Means and EM, for time series. The algorithm works by leveraging off the multi-resolution property of wavelets. The dilemma of choosing the initial centers is mitigated by initializing the centers at each approximation level, using the final centers returned by the coarser representations. In addition to casting t...

High-dimensional data has a major challenge due to the inherent sparsity of the points. Existing clustering algorithms are inefficient to the required similarity measure is computed between data points in the full-dimensional space. In this work, a number of projected clustering algorithms have been analyzed. However, most of them encounter difficulties when clusters hide in subspaces with very...

Clustering has been exten­ sively studied in data analy­ sis. However, clustering theories have thus far been unsatisfactory to justify use of the many proposed algorithms. A fun­ damental question is this: How do we define a cluster in a set of data points? In “A Mathematical Theory for Clus­ tering in Metric Spaces,” Cheng­Shang Chang and his colleagues attempt to answer this question by cons...

Clustering is the process of partitioning the data set into subsets called clusters, so that the data in each subset share some properties in common. Clustering is an important tool to explore the hidden structures of modern large Databases. Because of the huge variety of the problems and data distributions, different classical clustering algorithms, such as hierarchical, partitional, density-b...

Problem statement: Handling numerical data stored in a relational database has been performed differently from handling those numerical data stored in a single table due to the multiple occurrences (one-to-many association) of an individual record in the non-target table and non-determinate relations between tables. Numbers in Multi-Relational Data Mining (MRDM) were often discretized, after co...

Co-clustering, or simultaneous clustering of rows and columns of a two-dimensional data matrix, is rapidly becoming a powerful data analysis technique. Co-clustering has enjoyed wide success in varied application domains such as text clustering, gene-microarray analysis, natural language processing and image, speech and video analysis. In this paper, we introduce a partitional co-clustering for...

Objective type of Examination evaluation is easy in Computer world. But the descriptive type of question evaluation is more difficult and there is no significant research has been taken place. In this paper I propose a new solution to the above problem with text classification using the new fuzzy logic named CosFuzzy Logic. Document Clustering is a useful technique that organizes a large quanti...

Abstract In various scientific fields, researchers make use of partitioning methods (e.g., K -means) to disclose the structural mechanisms underlying object by variable data. some instances, however, a grouping objects into clusters that are allowed overlap (i.e., assigning multiple clusters) might lead better representation clustering structure. To obtain an overlapping from data, Mirkin’s ADd...

-Tbe applicability of evolution strategies (ESs), population based stochastic optimization techniques, to optimize clustering objective functions is explored. Clustering objective functions are categorized into centroid and non-centroid type of functions. Optimization of the centroid type of objective functions is accomplished by formulating them as functions of real-valued parameters using ESs...