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Fuzzy KMeans Clustering on a High Dimensional Semantic Space
"... Abstract. One way of representing semantics could be via a high dimensional conceptual space constructed by certain lexical semantic space models. Concepts (words), represented as a vector of other words in the semantic space, can be categorized via clustering techniques into a number of regions ref ..."
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clustering, which models the degree of an object belonging to certain cluster. This paper introduces a fuzzy kmeans clustering algorithm and how it is used to word clustering on the high dimensional semantic space constructed by a cognitively motivated semantic space model, namely Hyperspace Analogue
Xmeans: Extending Kmeans with Efficient Estimation of the Number of Clusters
 In Proceedings of the 17th International Conf. on Machine Learning
, 2000
"... Despite its popularity for general clustering, Kmeans suffers three major shortcomings; it scales poorly computationally, the number of clusters K has to be supplied by the user, and the search is prone to local minima. We propose solutions for the first two problems, and a partial remedy for the t ..."
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Cited by 412 (5 self)
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Despite its popularity for general clustering, Kmeans suffers three major shortcomings; it scales poorly computationally, the number of clusters K has to be supplied by the user, and the search is prone to local minima. We propose solutions for the first two problems, and a partial remedy
Mean shift, mode seeking, and clustering
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1995
"... AbstractMean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some kmeans like clustering algorithms its special cases. It is shown that mean shift is a modeseeki ..."
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Cited by 620 (0 self)
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AbstractMean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some kmeans like clustering algorithms its special cases. It is shown that mean shift is a mode
Verb Semantics And Lexical Selection
, 1994
"... ... structure. As Levin has addressed (Levin 1985), the decomposition of verbs is proposed for the purposes of accounting for systematic semanticsyntactic correspondences. This results in a series of problems for MT systems: inflexible verb sense definitions; difficulty in handling metaphor and new ..."
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Cited by 520 (4 self)
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... structure. As Levin has addressed (Levin 1985), the decomposition of verbs is proposed for the purposes of accounting for systematic semanticsyntactic correspondences. This results in a series of problems for MT systems: inflexible verb sense definitions; difficulty in handling metaphor
Probabilistic Latent Semantic Indexing
, 1999
"... Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized ..."
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Cited by 1207 (11 self)
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Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized
Probabilistic Latent Semantic Analysis
 In Proc. of Uncertainty in Artificial Intelligence, UAI’99
, 1999
"... Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of twomode and cooccurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Sema ..."
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Cited by 760 (9 self)
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Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of twomode and cooccurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 766 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Computing semantic relatedness using Wikipediabased explicit semantic analysis
 In Proceedings of the 20th International Joint Conference on Artificial Intelligence
, 2007
"... Computing semantic relatedness of natural language texts requires access to vast amounts of commonsense and domainspecific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a highdimensional space of concepts derived from Wikipedi ..."
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Cited by 546 (9 self)
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Computing semantic relatedness of natural language texts requires access to vast amounts of commonsense and domainspecific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a highdimensional space of concepts derived from
Refining Initial Points for KMeans Clustering
, 1998
"... Practical approaches to clustering use an iterative procedure (e.g. KMeans, EM) which converges to one of numerous local minima. It is known that these iterative techniques are especially sensitive to initial starting conditions. We present a procedure for computing a refined starting condition fro ..."
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Cited by 308 (5 self)
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Practical approaches to clustering use an iterative procedure (e.g. KMeans, EM) which converges to one of numerous local minima. It is known that these iterative techniques are especially sensitive to initial starting conditions. We present a procedure for computing a refined starting condition
The particel swarm: Explosion, stability, and convergence in a multidimensional complex space
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTION
"... The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained ..."
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Cited by 822 (10 self)
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The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately
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