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Published on Jun 25, 2006 in International Conference on Machine Learning
Fernando De la Torre38
Estimated H-index: 38
(Carnegie Mellon University),
Takeo Kanade122
Estimated H-index: 122
(Carnegie Mellon University)
Clustering is one of the most widely used statistical tools for data analysis. Among all existing clustering techniques, k-means is a very popular method because of its ease of programming and because it accomplishes a good trade-off between achieved performance and computational complexity. However, k-means is prone to local minima problems, and it does not scale too well with high dimensional data sets. A common approach to dealing with high dimensional data is to clust...
99 Citations Source Cite
Published on Jun 20, 2007 in International Conference on Machine Learning
Ludwig M. Busse2
Estimated H-index: 2
(ETH Zurich),
Peter Orbanz8
Estimated H-index: 8
(ETH Zurich),
Joachim M. Buhmann52
Estimated H-index: 52
(ETH Zurich)
Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic mo...
74 Citations Source Cite
Published on May 16, 1999 in International Conference on Software Engineering
Arie van Deursen35
Estimated H-index: 35
Tobias Kuipers17
Estimated H-index: 17
Many approaches to support (semi-automatic) identification of objects in legacy code take data structures as the starting point for candidate classes. Unfortunately, legacy data structures tend to grow over time, and may contain many unrelated fields at the time of migration. We propose a method for identifying objects by semi-automatically restructuring the legacy data structures. Issues involved include the selection of record fields of interest, the identification of procedures actually deali...
204 Citations Source Cite
Published on Nov 9, 2015 in International Conference on Neural Information Processing
Ruqi Zhang (Helsinki Institute for Information Technology), Zhirong Yang9
Estimated H-index: 9
(Helsinki Institute for Information Technology),
Jukka Corander43
Estimated H-index: 43
(Helsinki Institute for Information Technology)
Clustering or cluster analysis is an important and common task in data mining and analysis, with applications in many fields. However, most existing clustering methods are sensitive in the presence of limited amounts of data per cluster in real-world applications. Here we propose a new method called denoising cluster analysis to improve the accuracy. We first construct base clusterings with artificially corrupted data samples and lat...
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Published on Dec 1, 2016 in International Conference on Data Mining
Peter Laurinec2
Estimated H-index: 2
(Slovak University of Technology in Bratislava),
Marek Loderer4
Estimated H-index: 4
(Slovak University of Technology in Bratislava)
+ 3 AuthorsAnna Bou Ezzeddine4
Estimated H-index: 4
(Slovak University of Technology in Bratislava)
The paper presents an improvement of incremental adaptive power load forecasting methods by performing cluster analysis prior to forecasts. For clustering the centroid based method K-means, with K-means++ centroids initialization, was used. Ten various forecasting methods were compared in order to find the most suitable ones to combine with clustering. The used data set comes from Ireland, where half-hourly measurements of electricity consumption of more than 3600 hou...
4 Citations Source Cite
Published on Jul 1, 2001 in International Conference on Software Engineering
W. Dickinson1
Estimated H-index: 1
(Case Western Reserve University),
David Leon10
Estimated H-index: 10
(Case Western Reserve University),
A. Fodgurski1
Estimated H-index: 1
(Case Western Reserve University)
We experimentally evaluate the effectiveness of using cluster analysis of execution profiles to find failures among the executions induced by a set of potential test cases. We compare several filtering procedures for selecting executions to evaluate for conformance to requirements. Each filtering procedure involves a choice of a sampling strategy and a clustering metric. The results suggest that filtering procedures based on clustering are more effective than simple r...
226 Citations Source Cite
Published on Jan 1, 2015in Annals of the Rheumatic Diseases 12.35
Pascal Richette37
Estimated H-index: 37
(University of Paris),
Pierre Clerson21
Estimated H-index: 21
+ 2 AuthorsThomas Bardin44
Estimated H-index: 44
(University of Paris)
Objectives The reciprocal links between comorbidities and gout are complex. We used cluster analysis to attempt to identify different phenotypes on the basis of comorbidities in a large cohort of patients with gout. Methods This was a cross-sectional multicentre study of 2763 gout patients conducted from November 2010 to May 2011. Cluster analysis was conducted separately for variables and for observations in patients, measuring proximity between variables and identif...
48 Citations Source Cite
Published on Aug 1, 2010 in International Conference on Pattern Recognition
Yasushi Makihara20
Estimated H-index: 20
(Osaka University),
Yasushi Yagi29
Estimated H-index: 29
(Osaka University)
Pattern recognition problems often suffer from the larger intra-class variation due to situation variations such as pose, walking speed, and clothing variations in gait recognition. This paper describes a method of discriminant subspace analysis focused on situation cluster pair. In training phase, both a situation cluster discriminant subspace and class discriminant subspaces for the situation cluster pair by using training samples of non recognition-target classes. ...
3 Citations Source Cite
Published on Aug 26, 2001 in Knowledge Discovery and Data Mining
Joshua Zhexue Huang10
Estimated H-index: 10
(University of Hong Kong),
Michael K. Ng53
Estimated H-index: 53
(University of Hong Kong)
+ 2 AuthorsDavid W. Cheung42
Estimated H-index: 42
(University of Hong Kong)
Identification of the navigational patterns of casual visitors is an important step in online recommendation to convert casual visitors to customers in e-commerce. Clustering and sequential analysis are two primary techniques for mining navigational patterns from Web and application server logs. The characteristics of the log data and mining tasks require new data representation methods and analysis algorithms to be tested in the e-commerce environment. In this paper we pres...
17 Citations Source Cite
Published on Jul 10, 2006 in Intelligent Systems in Molecular Biology
Giovanni Bottegoni20
Estimated H-index: 20
Walter Rocchia6
Estimated H-index: 6
(Nest Labs)
+ 1 AuthorsAndrea Cavalli12
Estimated H-index: 12
Motivation: Sampling the conformational space is a fundamental step for both ligand-and structure-based drug design. However, the rational organization of different molecular conformations still remains a challenge. In fact, for drug design applications, the sampling process provides a redundant conformation set whose thorough analysis can be intensive, or even prohibitive. We propose a statistical approach based on cluster analysis aimed at rationalizing the output of metho...
27 Citations Source Cite