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514
Clustering and the continuous kmeans algorithm
 Los Alamos Science
, 1994
"... Many types of data analysis, such as the interpretation of Landsat images discussed in the accompanying article, involve datasets so large that their direct manipulation is impractical. Some method of data compression or consolidation must first be applied to reduce the size of the dataset without l ..."
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Cited by 59 (0 self)
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that consolidate data by clustering, or grouping, and then present a new method, the continuous kmeans algorithm, * developed at the Laboratory specifically for clustering large datasets. Clustering involves dividing a set of data points into nonoverlapping groups, or clusters, of points, where points in a
The effectiveness of lloydtype methods for the kmeans problem
 In FOCS
, 2006
"... We investigate variants of Lloyd’s heuristic for clustering high dimensional data in an attempt to explain its popularity (a half century after its introduction) among practitioners, and in order to suggest improvements in its application. We propose and justify a clusterability criterion for data s ..."
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Cited by 84 (3 self)
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sets. We present variants of Lloyd’s heuristic that quickly lead to provably nearoptimal clustering solutions when applied to wellclusterable instances. This is the first performance guarantee for a variant of Lloyd’s heuristic. The provision of a guarantee on output quality does not come
KMeans Clustering for Hidden Markov Models
 In Proceedings of the Seventh International Workshop on Frontiers in Handwriting Recognition
, 2000
"... An unsupervised kmeans clustering algorithm for hidden Markov models is described and applied to the task of generating subclass models for individual handwritten character classes. The algorithm is compared to a related clustering method and shown to give a relative change in the error rate of ..."
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Cited by 15 (0 self)
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An unsupervised kmeans clustering algorithm for hidden Markov models is described and applied to the task of generating subclass models for individual handwritten character classes. The algorithm is compared to a related clustering method and shown to give a relative change in the error rate
Classification and Clustering of User Mails by Using an Improved kmeans Clustering Algorithm
"... Abstract Kapproach clustering has been extensively used to advantage perception into organic systems from hugescale lifestyles science records. To quantify the similarities among biological facts units, Pearson correlation distance and standardized Euclidean distance are used maximum frequently; ..."
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; however, optimization techniques were in large part unexplored. Those two distance measurements are equivalent inside the feel that they yield the same okapproach clustering end result for same sets of ok preliminary centroids. for that reason, an efficient set of rules used for one is applicable
An alternative extension of the kmeans algorithm for clustering categorical data
 Int. J. Appl. Math. Comput. Sci
, 2004
"... Most of the earlier work on clustering has mainly been focused on numerical data whose inherent geometric properties can be exploited to naturally define distance functions between data points. Recently, the problem of clustering categorical data has started drawing interest. However, the computatio ..."
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Cited by 19 (0 self)
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, the computational cost makes most of the previous algorithms unacceptable for clustering very large databases. The kmeans algorithm is well known for its efficiency in this respect. At the same time, working only on numerical data prohibits them from being used for clustering categorical data. The main
Transform Residual Kmeans Trees for Scalable Clustering
"... Abstract—The Kmeans problem, i.e., to partition a dataset into K clusters is a fundamental problem common to numerous data mining applications. As it is an NPhard problem, iterative optimizations are typically used such as the Kmeans algorithm to compute cluster centers. As the Kmeans algorithm ..."
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cluster centers via Cartesian products of cluster centers in multiple groups or multiple stages, product and residual Kmeans trees reduce computation and storage complexity, making it possible to cluster large scale datasets. As residual Kmeans trees do not require assumed statistical independence among
A PrototypesEmbedded Genetic Kmeans Algorithm
"... This paper presents a genetic algorithm (GA) for Kmeans clustering. Instead of the widely applied stringofgroupnumbers encoding, we encode the prototypes of the clusters into the chromosomes. The crossover operator is designed to exchange prototypes between two chromosomes. The onestep Kmeans al ..."
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This paper presents a genetic algorithm (GA) for Kmeans clustering. Instead of the widely applied stringofgroupnumbers encoding, we encode the prototypes of the clusters into the chromosomes. The crossover operator is designed to exchange prototypes between two chromosomes. The onestep Kmeans
Kmeans Clustering Optimization Algorithm Based on MapReduce
"... Aiming at the defects of traditional Kmeans clustering algorithm for big data, this paper provides Kmeans clustering mining optimization algorithm based on big data, shows a MapReduce software architecture which is suitable for large data processing mechanism, provides an improved method for selec ..."
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for selecting initial clustering centers and puts forward a Kmeans algorithm optimization based on MapReduce model. The improved algorithm is applied to the coal quality analysis, the result shows that compared with traditional algorithms, the optimization algorithm improves the efficiency of the algorithm
An Efficient Unified KMeans Clustering Technique for Microarray Gene Expression Data
"... Abstract: Problem statement: Using microarray techniques one could monitor the expressions levels of thousands of genes simultaneously. One challenge was how to derive meaningful insights into expressed data. This might be carried out by clustering techniques such as hierarchical and kmeans, but mo ..."
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the results using correctness ratio and sum of squares criteria. A new approach suggested to addresses the primary issue of kmeans clustering algorithm that predefining number of clusters. This approach provides accurate number of clusters by minimizing the squared error function and maximizing
Comparative Analysis of KMeans and Fuzzy C Means Algorithms
"... Abstract—In the arena of software, data mining technology has been considered as useful means for identifying patterns and trends of large volume of data. This approach is basically used to extract the unknown pattern from the large set of data for business as well as real time applications. It is a ..."
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are used to solve this problem. In this research work two important clustering algorithms namely centroid based KMeans and representative object based FCM (Fuzzy CMeans) clustering algorithms are compared. These algorithms are applied and performance is evaluated on the basis of the efficiency
Results 1  10
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514