Results 181 to 190 of about 216,194 (219)

Incremental Clustering for Hierarchical Clustering

2018 5th International Conference on Computational Science/ Intelligence and Applied Informatics (CSII), 2018
This paper proposes a clustering algorithm for updating clusters without reclustering when a point is inserted. We define the center and the radius of the cluster, and update clustering results of points using them. We introduce the concept of outliers and also consider the change in the number of clusters caused by data insertion.
Kakeru Narita, Teruhisa Hochin, Hiroki Nomiya   +2 more
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Personalized Hierarchical Clustering

2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06), 2006
A hierarchical structure can provide efficient access to information contained in a collection of documents. However, such a structure is not always available, e.g. for a set of documents a user has collected over time in a single folder or the results of a web search.
Korinna Bade, Andreas Nürnberger
openaire   +1 more source

Bayesian hierarchical clustering

Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005
We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages over traditional distance-based agglomerative clustering algorithms. (1) It defines a probabilistic model of the data which can be used to compute the predictive distribution of ...
Katherine A. Heller, Zoubin Ghahramani
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Hierarchical Ensemble Clustering

2010 IEEE International Conference on Data Mining, 2010
Ensemble clustering has emerged as an important elaboration of the classical clustering problems. Ensemble clustering refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better fit in some sense than the existing ...
Li Zheng 0001, Tao Li 0001, Chris H. Q. Ding   +2 more
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Hierarchical video clustering

IEEE 6th Workshop on Multimedia Signal Processing, 2004., 2005
We present a novel generative model for video that models video as mixture of transformed video scenes. The learning procedure automatically clusters video frames into video scenes and objects. The learning algorithm is based on a hierarchical, on-line EM algorithm.
Nemanja Petrovic, Nebojsa Jojic, Thomas S. Huang   +2 more
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Hierarchical Clustering Schemes

Psychometrika, 1967
Techniques for partitioning objects into optimally homogeneous groups on the basis of empirical measures of similarity among those objects have received increasing attention in several different fields. This paper develops a useful correspondence between any hierarchical system of such clusters, and a particular type of distance measure.
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Stability of a hierarchical clustering

Pattern Recognition, 1980
Abstract Clustering algorithms have the annoying habit of finding clusters even when the data are generated randomly. Verifying that potential clusterings are real in some objective sense is receiving more attention as the number of new clustering algorithms and their applications grow.
Stephen P. Smith, Richard C. Dubes
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On-line hierarchical clustering

Pattern Recognition Letters, 1998
Summary: Most of the techniques used in the literature for hierarchical clustering are based on off-line operation. The main contribution of this paper is to propose a new algorithm for on-line hierarchical clustering by finding the nearest \(k\) objects to each introduced object so far and these nearest \(k\) objects are continuously updated by the ...
Yasser El-Sonbaty, Mohamed A. Ismail
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Introduction to Hierarchical Clustering

Journal of Clinical Neurophysiology, 2002
Hierarchical clustering of spike events is a method of grouping events that are similar in topology, morphology, or both, and it provides a method of efficient, detailed analysis of interictal events. Information about the relative populations of spikes at multiple foci is presented, and artifact events are grouped and eliminated en masse.
Michael J, Guess, Scott B, Wilson
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Compressed Hierarchical Clustering

2018 Data Compression Conference, 2018
Hierarchical Clustering is widely used in Machine Learning and Data Mining. It stores bit-vectors in the nodes of a k-ary tree, usually without trying to compress them. We suggest a double usage of the {\sf xor}ing operations defining the Hamming distance used in the clustering process, extending it also to be used to transform the vector in one node ...
Gilad Baruch, Dana Shapira, Shmuel T. Klein   +2 more
openaire   +1 more source