Results 1 - 10 of 798

Automatically characterizing large scale program behavior

by Timothy Sherwood, Erez Perelman, Greg Hamerly , 2002
"... Understanding program behavior is at the foundation of computer architecture and program optimization. Many pro-grams have wildly different behavior on even the very largest of scales (over the complete execution of the program). This realization has ramifications for many architectural and com-pile ..."
Abstract - Cited by 778 (41 self) - Add to MetaCart
of algorithms based on clustering capable of an-alyzing this behavior. We then demonstrate an application of this technology to automatically determine where to simulate for a program to help guide computer architecture research. 1.

Web Document Clustering: A Feasibility Demonstration

by Oren Zamir, Oren Etzioni , 1998
"... Abstract Users of Web search engines are often forced to sift through the long ordered list of document “snippets” returned by the engines. The IR community has explored document clustering as an alternative method of organizing retrieval results, but clustering has yet to be deployed on the major s ..."
Abstract - Cited by 435 (3 self) - Add to MetaCart
search engines. The paper articulates the unique requirements of Web document clustering and reports on the first evaluation of clustering methods in this domain. A key requirement is that the methods create their clusters based on the short snippets returned by Web search engines. Surprisingly, we find

A spectral clustering approach to finding communities in graphs

by Scott White, Padhraic Smyth - IN SIAM INTERNATIONAL CONFERENCE ON DATA MINING , 2005
"... Clustering nodes in a graph is a useful general technique in data mining of large network data sets. In this context, Newman and Girvan [9] recently proposed an objective function for graph clustering called the Q function which allows automatic selection of the number of clusters. Empirically, high ..."
Abstract - Cited by 167 (0 self) - Add to MetaCart
that the new algorithms are efficient and effective at finding both good clusterings and the appropriate number of clusters across a variety of real-world graph data sets. In addition, the spectral algorithms are much faster for large sparse graphs, scaling roughly linearly with the number of nodes n

RNA polymerase III transcribes human microRNAs.

by Glen M Borchert , William Lanier , Beverly L Davidson - Nature Struct. Mol. Biol. , 2006
"... Prior work demonstrates that mammalian microRNA (miRNA or miR) expression requires RNA polymerase II (Pol II). However, the transcriptional requirements of many miRNAs remain untested. Our genomic analysis of miRNAs in the human chromosome 19 miRNA cluster (C19MC) revealed that they are intersperse ..."
Abstract - Cited by 201 (3 self) - Add to MetaCart
(17-25 base pairs (bp)) noncoding RNAs that guide cellular machinery to specific messenger RNAs 2,3 to control expression. Initial miRNA transcripts can be several thousand base pairs in length, and they are processed to produce B70-bp stem-loops (pre-miRNAs) before nuclear export 4 . Upon entering

Improved annotation of the blogosphere via autotagging and hierarchical clustering

by Christopher H. Brooks , 2006
"... Tags have recently become popular as a means of annotating and organizing Web pages and blog entries. Advocates of tagging argue that the use of tags produces a ’folksonomy’, a system in which the meaning of a tag is determined by its use among the community as a whole. We analyze the effectiveness ..."
Abstract - Cited by 120 (1 self) - Add to MetaCart
that automatically extracting words deemed to be highly relevant can produce a more focused categorization of articles. We also show that clustering algorithms can be used to reconstruct a topical hierarchy among tags, and suggest that these approaches may be used to address some of the weaknesses in current tagging

Clustering with a Genetically Optimized Approach

by L. O. Hall, B. Ozyurt, J. C. Bezdek - IEEE Transactions on Evolutionary Computation , 1999
"... This paper describes a genetically guided approach to optimizing the hard (J1) and fuzzy (Jm) c-means functionals used in cluster analysis. Our experiments show that a genetic algorithm ameliorates the difficulty of choosing an initialization for the c-means clustering algorithms. Experiments use si ..."
Abstract - Cited by 96 (1 self) - Add to MetaCart
This paper describes a genetically guided approach to optimizing the hard (J1) and fuzzy (Jm) c-means functionals used in cluster analysis. Our experiments show that a genetic algorithm ameliorates the difficulty of choosing an initialization for the c-means clustering algorithms. Experiments use

Genetic K-means Algorithm

by K. Krishna, M. Narasimha Murty - IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS, PART B: CYBERNETICS , 1999
"... In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA’s used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a ..."
Abstract - Cited by 93 (0 self) - Add to MetaCart
In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA’s used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes

Finding the Optimal Number of Clusters Using Genetic Algorithms

by Yongguo Liu , Mao Ye , Jun Peng , Hong Wu
"... Abstract-In clustering analysis, many methods require the designer to provide the number of clusters. Unfortunately, the designer has no idea, in general, about this information beforehand. In this paper, we propose a genetic algorithm based clustering method called Automatic Genetic Clustering for ..."
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Abstract-In clustering analysis, many methods require the designer to provide the number of clusters. Unfortunately, the designer has no idea, in general, about this information beforehand. In this paper, we propose a genetic algorithm based clustering method called Automatic Genetic Clustering

An Efficient DBSCAN using Genetic Algorithm based Clustering

by Lovely Sharma, Prof K. Ramya
"... Abstract — Data mining is widely employed in business management and engineering. The major objective of data mining is to discover helpful and accurate information among a vast quantity of data, providing a orientation basis for decision makers. Data clustering is currently a very popular and frequ ..."
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and frequently applied analytical method in data mining. DBSCAN is a traditional and widely-accepted density-based clustering method. It is used to find clusters of arbitrary shapes and sizes yet may have trouble with clusters of varying density. In this paper an efficient DBSCAN clustering using genetic

Defining and Evaluating Network Communities based on Ground-truth. Extended version

by Jaewon Yang, Jure Leskovec , 2012
"... Abstract—Nodes in real-world networks organize into densely linked communities where edges appear with high concentration among the members of the community. Identifying such communities of nodes has proven to be a challenging task mainly due to a plethora of definitions of a community, intractabili ..."
Abstract - Cited by 112 (4 self) - Add to MetaCart
join various interest based social groups. We use such groups to define a reliable and robust notion of ground-truth communities. We then propose a methodology which allows us to compare and quantitatively evaluate how different structural definitions of network communities correspond to ground
Results 1 - 10 of 798