The Experts below are selected from a list of 19242 Experts worldwide ranked by ideXlab platform
Junzo Watada - One of the best experts on this subject based on the ideXlab platform.
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decomposition of term Document Matrix representation for clustering analysis
IEEE International Conference on Fuzzy Systems, 2011Co-Authors: Jianxiong Yang, Junzo WatadaAbstract:Latent Semantic Indexing (LSI) is an information retrieval technique using a low-rank singular value decomposition (SVD) of term-Document Matrix. The aim of this method is to reduce the Matrix dimension by finding a pattern in Document collection with concurrently referring terms. The methods are implemented to calculate the weight of term-Document in vector space model (VSM) for Document clustering using fuzzy clustering algorithm. LSI is an attempt to exploit the underlying semantic structure of word usage in Documents. During the query-matching phase of LSI, a user's query is first projected into the term-Document space, and then compared to all terms and Documents represented in the vector space. Using some similarity measure, the nearest (most relevant) terms and Documents are identified and returned to the user. The current LSI query-matching method requires computing the similarity measure about the query of every term and Document in the vector space. In this paper, the Maximal Tree Algorithm is used within a recent LSI implementation to mitigate the computational time and computational complexity of query matching. The Maximal Tree data structure stores the term and Document vectors in such a way that only those terms and Documents are most likely qualified as the nearest neighbor to the query will be examined and retrieved. In a word, this novel algorithm is suitable for improving the accuracy of data miners.
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FUZZ-IEEE - Decomposition of term-Document Matrix representation for clustering analysis
2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011Co-Authors: Jianxiong Yang, Junzo WatadaAbstract:Latent Semantic Indexing (LSI) is an information retrieval technique using a low-rank singular value decomposition (SVD) of term-Document Matrix. The aim of this method is to reduce the Matrix dimension by finding a pattern in Document collection with concurrently referring terms. The methods are implemented to calculate the weight of term-Document in vector space model (VSM) for Document clustering using fuzzy clustering algorithm. LSI is an attempt to exploit the underlying semantic structure of word usage in Documents. During the query-matching phase of LSI, a user's query is first projected into the term-Document space, and then compared to all terms and Documents represented in the vector space. Using some similarity measure, the nearest (most relevant) terms and Documents are identified and returned to the user. The current LSI query-matching method requires computing the similarity measure about the query of every term and Document in the vector space. In this paper, the Maximal Tree Algorithm is used within a recent LSI implementation to mitigate the computational time and computational complexity of query matching. The Maximal Tree data structure stores the term and Document vectors in such a way that only those terms and Documents are most likely qualified as the nearest neighbor to the query will be examined and retrieved. In a word, this novel algorithm is suitable for improving the accuracy of data miners.
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ADMI - Wise search engine based on LSI
Lecture Notes in Computer Science, 2010Co-Authors: Yang Jianxiong, Junzo WatadaAbstract:The objective of this work is to provide, as a search engine, latent semantic indexing (LSI), which is a classical method to produce optimal approximations of a term-Document Matrix and has been used for textual information mining. The use of this technique is examining mine content which based web Document, using keyword features of Documents. Experimental results show that together with both textual and latent features LSI can extract the underlying semantic structure of web Documents, thus improve the search engine performance significantly.
Elizabeth Leon - One of the best experts on this subject based on the ideXlab platform.
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web Document clustering based on a new niching memetic algorithm term Document Matrix and bayesian information criterion
Congress on Evolutionary Computation, 2010Co-Authors: Carlos Cobos, Claudia Montealegre, Mariafernanda Mejia, Martha Mendoza, Elizabeth LeonAbstract:This paper introduces a new description-centric algorithm for web Document clustering based on Memetic Algorithms with Niching Methods, Term-Document Matrix and Bayesian Information Criterion. The algorithm defines the number of clusters automatically. The Memetic Algorithm provides a combined global and local strategy for a search in the solution space and the Niching methods to promote diversity in the population and prevent the population from converging too quickly (based on restricted competition replacement and restrictive mating). The Memetic Algorithm uses the K-means algorithm to find the optimum value in a local search space. Bayesian Information Criterion is used as a fitness function, while FP-Growth is used to reduce the high dimensionality in the vocabulary. This resulting algorithm, called WDC-NMA, was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm). Also, it was also then initially evaluated by a group of users.
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IEEE Congress on Evolutionary Computation - Web Document clustering based on a new niching Memetic Algorithm, Term-Document Matrix and Bayesian Information Criterion
IEEE Congress on Evolutionary Computation, 2010Co-Authors: Carlos Cobos, Claudia Montealegre, Mariafernanda Mejia, Martha Mendoza, Elizabeth LeonAbstract:This paper introduces a new description-centric algorithm for web Document clustering based on Memetic Algorithms with Niching Methods, Term-Document Matrix and Bayesian Information Criterion. The algorithm defines the number of clusters automatically. The Memetic Algorithm provides a combined global and local strategy for a search in the solution space and the Niching methods to promote diversity in the population and prevent the population from converging too quickly (based on restricted competition replacement and restrictive mating). The Memetic Algorithm uses the K-means algorithm to find the optimum value in a local search space. Bayesian Information Criterion is used as a fitness function, while FP-Growth is used to reduce the high dimensionality in the vocabulary. This resulting algorithm, called WDC-NMA, was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm). Also, it was also then initially evaluated by a group of users.
Gregory B Newby - One of the best experts on this subject based on the ideXlab platform.
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instability of relevance ranked results using latent semantic indexing for web search
Hawaii International Conference on System Sciences, 2010Co-Authors: Houssain Kettani, Gregory B NewbyAbstract:The latent semantic indexing (LSI) methodology for information retrieval applies the singular value decomposition to identify an eigensystem for a large Matrix, in which cells represent the occurrence of terms (words) within Documents. This methodology is used to rank text Documents, such as Web pages or abstracts, based on their relevance to a topic. The LSI was introduced to address the issues of synonymy (different words with the same meaning) and polysemy (the same words with multiple meanings), thus addressing the ambiguity in human language by utilizing the statistical context of words. Rather than keeping all k possible eigenvectors and eigenvalues from the singular value decomposition which approximates the original term by Document Matrix, a smaller number is used - essentially allowing a fuzzy match of a topic to the original term by Document Matrix. In this paper, we show that the choice k impacts the resultant ranking and there is no value of k that results in stability of ranked results for similarity of the topic to Documents. This is a surprising result, because prior literature indicates that eigensystems based on successively large values of k should approximate the complete (max k) eigensystems. The finding that Document-query similarity rankings with larger values of k do not, in fact, maintain consistency, makes it difficult to assert that any particular value of k is optimal. This in turn renders LSI potentially untrustworthy for use in ranking text Documents, even for values that differ by only 10% of the max k.
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HICSS - Instability of Relevance-Ranked Results Using Latent Semantic Indexing for Web Search
2010 43rd Hawaii International Conference on System Sciences, 2010Co-Authors: Houssain Kettani, Gregory B NewbyAbstract:The latent semantic indexing (LSI) methodology for information retrieval applies the singular value decomposition to identify an eigensystem for a large Matrix, in which cells represent the occurrence of terms (words) within Documents. This methodology is used to rank text Documents, such as Web pages or abstracts, based on their relevance to a topic. The LSI was introduced to address the issues of synonymy (different words with the same meaning) and polysemy (the same words with multiple meanings), thus addressing the ambiguity in human language by utilizing the statistical context of words. Rather than keeping all k possible eigenvectors and eigenvalues from the singular value decomposition which approximates the original term by Document Matrix, a smaller number is used - essentially allowing a fuzzy match of a topic to the original term by Document Matrix. In this paper, we show that the choice k impacts the resultant ranking and there is no value of k that results in stability of ranked results for similarity of the topic to Documents. This is a surprising result, because prior literature indicates that eigensystems based on successively large values of k should approximate the complete (max k) eigensystems. The finding that Document-query similarity rankings with larger values of k do not, in fact, maintain consistency, makes it difficult to assert that any particular value of k is optimal. This in turn renders LSI potentially untrustworthy for use in ranking text Documents, even for values that differ by only 10% of the max k.
Jianxiong Yang - One of the best experts on this subject based on the ideXlab platform.
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decomposition of term Document Matrix representation for clustering analysis
IEEE International Conference on Fuzzy Systems, 2011Co-Authors: Jianxiong Yang, Junzo WatadaAbstract:Latent Semantic Indexing (LSI) is an information retrieval technique using a low-rank singular value decomposition (SVD) of term-Document Matrix. The aim of this method is to reduce the Matrix dimension by finding a pattern in Document collection with concurrently referring terms. The methods are implemented to calculate the weight of term-Document in vector space model (VSM) for Document clustering using fuzzy clustering algorithm. LSI is an attempt to exploit the underlying semantic structure of word usage in Documents. During the query-matching phase of LSI, a user's query is first projected into the term-Document space, and then compared to all terms and Documents represented in the vector space. Using some similarity measure, the nearest (most relevant) terms and Documents are identified and returned to the user. The current LSI query-matching method requires computing the similarity measure about the query of every term and Document in the vector space. In this paper, the Maximal Tree Algorithm is used within a recent LSI implementation to mitigate the computational time and computational complexity of query matching. The Maximal Tree data structure stores the term and Document vectors in such a way that only those terms and Documents are most likely qualified as the nearest neighbor to the query will be examined and retrieved. In a word, this novel algorithm is suitable for improving the accuracy of data miners.
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FUZZ-IEEE - Decomposition of term-Document Matrix representation for clustering analysis
2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011Co-Authors: Jianxiong Yang, Junzo WatadaAbstract:Latent Semantic Indexing (LSI) is an information retrieval technique using a low-rank singular value decomposition (SVD) of term-Document Matrix. The aim of this method is to reduce the Matrix dimension by finding a pattern in Document collection with concurrently referring terms. The methods are implemented to calculate the weight of term-Document in vector space model (VSM) for Document clustering using fuzzy clustering algorithm. LSI is an attempt to exploit the underlying semantic structure of word usage in Documents. During the query-matching phase of LSI, a user's query is first projected into the term-Document space, and then compared to all terms and Documents represented in the vector space. Using some similarity measure, the nearest (most relevant) terms and Documents are identified and returned to the user. The current LSI query-matching method requires computing the similarity measure about the query of every term and Document in the vector space. In this paper, the Maximal Tree Algorithm is used within a recent LSI implementation to mitigate the computational time and computational complexity of query matching. The Maximal Tree data structure stores the term and Document vectors in such a way that only those terms and Documents are most likely qualified as the nearest neighbor to the query will be examined and retrieved. In a word, this novel algorithm is suitable for improving the accuracy of data miners.
Carlos Cobos - One of the best experts on this subject based on the ideXlab platform.
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web Document clustering based on a new niching memetic algorithm term Document Matrix and bayesian information criterion
Congress on Evolutionary Computation, 2010Co-Authors: Carlos Cobos, Claudia Montealegre, Mariafernanda Mejia, Martha Mendoza, Elizabeth LeonAbstract:This paper introduces a new description-centric algorithm for web Document clustering based on Memetic Algorithms with Niching Methods, Term-Document Matrix and Bayesian Information Criterion. The algorithm defines the number of clusters automatically. The Memetic Algorithm provides a combined global and local strategy for a search in the solution space and the Niching methods to promote diversity in the population and prevent the population from converging too quickly (based on restricted competition replacement and restrictive mating). The Memetic Algorithm uses the K-means algorithm to find the optimum value in a local search space. Bayesian Information Criterion is used as a fitness function, while FP-Growth is used to reduce the high dimensionality in the vocabulary. This resulting algorithm, called WDC-NMA, was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm). Also, it was also then initially evaluated by a group of users.
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IEEE Congress on Evolutionary Computation - Web Document clustering based on a new niching Memetic Algorithm, Term-Document Matrix and Bayesian Information Criterion
IEEE Congress on Evolutionary Computation, 2010Co-Authors: Carlos Cobos, Claudia Montealegre, Mariafernanda Mejia, Martha Mendoza, Elizabeth LeonAbstract:This paper introduces a new description-centric algorithm for web Document clustering based on Memetic Algorithms with Niching Methods, Term-Document Matrix and Bayesian Information Criterion. The algorithm defines the number of clusters automatically. The Memetic Algorithm provides a combined global and local strategy for a search in the solution space and the Niching methods to promote diversity in the population and prevent the population from converging too quickly (based on restricted competition replacement and restrictive mating). The Memetic Algorithm uses the K-means algorithm to find the optimum value in a local search space. Bayesian Information Criterion is used as a fitness function, while FP-Growth is used to reduce the high dimensionality in the vocabulary. This resulting algorithm, called WDC-NMA, was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm). Also, it was also then initially evaluated by a group of users.