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Enhanced Cluster Centroid Determination in K means Algorithm
"... Abstract — Clustering is the most important unsupervised learning technique of organizing objects into groups whose members are similar in some way. Partitional clustering algorithms obtain a single partition of the data instead of a clustering structure. Kmean clustering is a common approach; howe ..."
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; however one of its drawbacks is the selection of initial centroid points randomly because of which algorithm has to reiterate number of times. This paper first studies existing methods for selecting the number of clusters and initial centroid points, and also proposes a new method for selecting
New Method for Finding Initial Cluster Centroids in Kmeans Algorithm
"... Data Mining is special field of computer science concerned with the automated extraction of patterns of knowledge implicitly stored in large databases, data warehouses and other large data repositories. Clustering is one of the Data Mining tasks which is used to cluster objects on the basis of their ..."
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Cited by 1 (0 self)
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depends upon the chosen central values for clustering. So accuracy of the K Means algorithm depends much on the chosen central values. The original K Means method chooses the initial cluster centroids randomly which affects its performance. This paper presents a new method for finding initial cluster
A CLUSTER CENTROID METHOD FOR ROOM RESPONSE EQUALIZATION AT MULTIPLE LOCATIONS
"... In this paper we address the problem of simultaneous room response equalization for multiple listeners. Traditional approaches to this problem have used a single microphone at the listening position to measure impulse responses from a loudspeaker and then use an inverse filter to correct the frequen ..."
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Cited by 4 (2 self)
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propose a new approach that is based on the Fuzzy cmeans clustering technique. We use this method to design equalization filters and demonstrate that we can achieve better equalization performance for several locations in the room simultaneously as compared to single point or simple averaging methods. 1.
On Spectral Clustering: Analysis and an algorithm
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
, 2001
"... Despite many empirical successes of spectral clustering methods  algorithms that cluster points using eigenvectors of matrices derived from the distances between the points  there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
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Cited by 1697 (13 self)
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Despite many empirical successes of spectral clustering methods  algorithms that cluster points using eigenvectors of matrices derived from the distances between the points  there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors
CentroidBased Summarization of Multiple Documents: Sentence Extraction, UtilityBased Evaluation, and User Studies
, 2000
"... We present a multidocument summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also des.cdbe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single and multipl ..."
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Cited by 350 (19 self)
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We present a multidocument summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also des.cdbe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single
Distance Metric Learning, With Application To Clustering With SideInformation
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 15
, 2003
"... Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may be for the us ..."
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Cited by 799 (14 self)
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Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may
Scatter/Gather: A Clusterbased Approach to Browsing Large Document Collections
, 1992
"... Document clustering has not been well received as an information retrieval tool. Objections to its use fall into two main categories: first, that clustering is too slow for large corpora (with running time often quadratic in the number of documents); and second, that clustering does not appreciably ..."
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Cited by 772 (12 self)
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Document clustering has not been well received as an information retrieval tool. Objections to its use fall into two main categories: first, that clustering is too slow for large corpora (with running time often quadratic in the number of documents); and second, that clustering does not appreciably
Clustering with Bregman Divergences
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... A wide variety of distortion functions are used for clustering, e.g., squared Euclidean distance, Mahalanobis distance and relative entropy. In this paper, we propose and analyze parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergence ..."
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Cited by 441 (59 self)
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divergences. The proposed algorithms unify centroidbased parametric clustering approaches, such as classical kmeans and informationtheoretic clustering, which arise by special choices of the Bregman divergence. The algorithms maintain the simplicity and scalability of the classical kmeans algorithm, while
Estimating the number of clusters in a dataset via the Gap statistic
, 2000
"... We propose a method (the \Gap statistic") for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. kmeans or hierarchical), comparing the change in within cluster dispersion to that expected under an appropriate reference ..."
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Cited by 492 (1 self)
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We propose a method (the \Gap statistic") for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. kmeans or hierarchical), comparing the change in within cluster dispersion to that expected under an appropriate reference
Reexamining the Cluster Hypothesis: Scatter/Gather on Retrieval Results
, 1996
"... We present Scatter/Gather, a clusterbased document browsing method, as an alternative to ranked titles for the organization and viewing of retrieval results. We systematically evaluate Scatter/Gather in this context and find significant improvements over similarity search ranking alone. This resul ..."
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Cited by 469 (5 self)
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We present Scatter/Gather, a clusterbased document browsing method, as an alternative to ranked titles for the organization and viewing of retrieval results. We systematically evaluate Scatter/Gather in this context and find significant improvements over similarity search ranking alone
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