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A comparison of document clustering techniques
 In KDD Workshop on Text Mining
, 2000
"... This paper presents the results of an experimental study of some common document clustering techniques: agglomerative hierarchical clustering and Kmeans. (We used both a “standard” Kmeans algorithm and a “bisecting ” Kmeans algorithm.) Our results indicate that the bisecting Kmeans technique is ..."
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Cited by 613 (27 self)
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This paper presents the results of an experimental study of some common document clustering techniques: agglomerative hierarchical clustering and Kmeans. (We used both a “standard” Kmeans algorithm and a “bisecting ” Kmeans algorithm.) Our results indicate that the bisecting Kmeans technique
HYBRID BISECT KMEANS CLUSTERING ALGORITHM
"... Abstract—In this paper, we present a hybrid clustering algorithm that combines divisive and agglomerative hierarchical clustering algorithm. Our method uses bisect Kmeans for divisive clustering algorithm and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) for agglomerative clustering al ..."
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Abstract—In this paper, we present a hybrid clustering algorithm that combines divisive and agglomerative hierarchical clustering algorithm. Our method uses bisect Kmeans for divisive clustering algorithm and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) for agglomerative clustering
Bisecting KMeans for Clustering Web Log data
"... Web usage mining is the area of web mining which deals with extraction of useful knowledge from web log information produced by web servers. One of the most important tasks of Web Usage Mining (WUM) is web user clustering which forms groups of users exhibiting similar interests or similar browsing p ..."
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patterns. This paper presents results of clustering techniques for Web log data using Kmeans and Bisecting Kmeans algorithm. Clusters are formed with respect to similar IP address and packet combinations. The clustering framework is further used as an approach for intrusion detection from the log files
bisecting divisive clustering algorithms
"... Abstract. This paper deals with the problem of clustering a dataset. In particular, the bisecting divisive approach is here considered. This approach can be naturally divided into two subproblems: the problem of choosing which cluster must be divided, and the problem of splitting the selected clus ..."
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bisecting Kmeans algorithm, and the recently proposed Principal Direction Divisive Partitioning (PDDP) algorithm. The problem of evaluating the quality of a partition is also discussed.
Kmeans++: The advantages of careful seeding.
 In Proceedings of the Eighteenth Annual ACMSIAM Symposium on Discrete Algorithms, SODA ’07,
, 2007
"... Abstract The kmeans method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting kmeans with a very simple, ran ..."
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Cited by 478 (8 self)
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, randomized seeding technique, we obtain an algorithm that is Θ(log k)competitive with the optimal clustering. Preliminary experiments show that our augmentation improves both the speed and the accuracy of kmeans, often quite dramatically.
Constrained Kmeans Clustering with Background Knowledge
 In ICML
, 2001
"... Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular kmeans clustering algorithm can be pro tably modi ed ..."
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Cited by 488 (9 self)
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Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular kmeans clustering algorithm can be pro tably modi ed
An Efficient kMeans Clustering Algorithm: Analysis and Implementation
, 2000
"... Kmeans clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its ..."
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Cited by 417 (4 self)
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nearest center. A popular heuristic for kmeans clustering is Lloyd's algorithm. In this paper we present a simple and efficient implementation of Lloyd's kmeans clustering algorithm, which we call the filtering algorithm. This algorithm is very easy to implement. It differs from most other
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 418 (5 self)
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) measure. The innovations include two new ways of exploiting cached sufficient statistics and a new very efficient test that in one Kmeans sweep selects the most promising subset of classes for refinement. This gives rise to a fast, statistically founded algorithm that outputs both the number of classes
Inferring User Search goals Engine Using Bisecting Algorithm
"... Abstract — Different users may have different search goals when they submit broadtopic and ambiguous query, to a search engine. The inference and analysis of user search goals can be very useful in improving performance of search engine. To infer user search goals by analyzing search engine query l ..."
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novel approach to generate pseudodocuments by using feedback sessions for clustering. For clustering we use a new algorithm which is bisecting Kmeans algorithm. At the end, a new criterion “Classified Average Precision (CAP) ” is proposed to evaluate the performance of inferring user search goals.
KSVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
, 2006
"... In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signalatoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and inc ..."
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Cited by 935 (41 self)
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signal representations. Given a set of training signals, we seek the dictionary that leads to the best representation for each member in this set, under strict sparsity constraints. We present a new method—the KSVD algorithm—generalizing the umeans clustering process. KSVD is an iterative method
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