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Efficiently clustering transactional data with weighted coverage density

Hua Yan, Keke Chen, Ling Liu
2006 Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06  
First, we use the concept of Weighted Coverage Density as a categorical similarity measure for efficient clustering of transactional datasets.  ...  Second, we develop two transactional data clustering specific evaluation metrics based on the concept of large transactional items and the coverage density respectively.  ...  First, we introduce the concept of Weighted Coverage Density (WCD) as intuitive categorical similarity measure for efficient clustering of transactional datasets.  ... 
doi:10.1145/1183614.1183668 dblp:conf/cikm/YanCL06 fatcat:k4qfrrozsbeqxe27ayjayazw3i

SCALE: a scalable framework for efficiently clustering transactional data

Hua Yan, Keke Chen, Ling Liu, Zhang Yi
2009 Data mining and knowledge discovery  
Second, we develop the weighted coverage density measure based clustering algorithm, a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data.  ...  First, we introduce the concept of Weighted Coverage Density as a categorical similarity measure for efficient clustering of transactional datasets.  ...  First, we introduce the concept of Weighted Coverage Density (WCD) as intuitive categorical similarity measure for efficient clustering of transactional datasets.  ... 
doi:10.1007/s10618-009-0134-5 fatcat:xr3iicgtovfw7frrjbnzdlgbvi

F-tree: an algorithm for clustering transactional data using frequency tree [article]

Mahmoud Mahdi, Samir Abdelrahman, Reem Bahgat, Ismail Ismail
2017 arXiv   pre-print
In this research, we study the problem of categorical data clustering for transactional data characterized with high dimensionality and large volume.  ...  Thirdly, the proposed evaluation metric used efficiently to solve the overlapping of transaction items generates high-quality clustering results.  ...  Efficiently Clustering Transactional Data with Weighted Coverage Density WCD Weighted coverage Density WCD algorithm introduces a new concept beside the clustering algorithm.  ... 
arXiv:1705.00761v1 fatcat:yea5apzgm5dblclprqpj35ytt4

Evaluating the Impact of Software Evolution on Software Clustering

Fabian Beck, Stephan Diehl
2010 2010 17th Working Conference on Reverse Engineering  
But still the traditional approach provides better results because of a more reliable data density of the structural data.  ...  To fill this gap, we conducted several clustering experiments with an approved software clustering tool comparing and combining the evolutionary and the structural approach.  ...  Additionally, the data density is expressed as the percentage of nodes with at least one in-or outgoing edge (second value), which we denote as node coverage. 1) Dependency Efficiency: The average values  ... 
doi:10.1109/wcre.2010.19 dblp:conf/wcre/BeckD10 fatcat:leyg3r7vunaibfshtjsf4lel24

Determining the best K for clustering transactional datasets: A coverage density-based approach

Hua Yan, Keke Chen, Ling Liu, Joonsoo Bae
2009 Data & Knowledge Engineering  
Concretely, we propose Transactional-cluster-modes Dissimilarity based on the concept of coverage density as an intuitive transactional inter-cluster dissimilarity measure.  ...  find the candidate optimal number Ks of clusters of transactional data.  ...  Concept of Coverage Density In this section we briefly introduce the concept of Coverage Density (CD) [?] .  ... 
doi:10.1016/j.datak.2008.08.005 fatcat:i3h7ttmfwzbyjei6uoe7qxg52e

Multi-Objective Optimization to Identify High Quality Clusters with Close Referential Point using Evolutionary Clustering Techniques

M. Anusha
2018 Asian Journal of Computer Science and Technology  
The resultant clusters were analysed and validated using cluster validity indexes. The proposed algorithm is tested with several UCI real-life data sets.  ...  The aim of this research paper is to solve a multi-objective optimization algorithm with close reference point learning method to identify high quality data clusters.  ...  The efficiency of QP-ECMO for identifying high quality data clusters is given in Table II .  ... 
doi:10.51983/ajcst-2018.7.3.1894 fatcat:jxls3wernrh45jjxf4xszax4zm

Software-Defined Management Model for Energy-Aware Vehicular Networks

Elif Bozkaya, Berk Canberk
2017 EAI Endorsed Transactions on Wireless Spectrum  
In the considered scenario, high vehicular mobility and limited coverage area of RSUs cause a degradation in Quality of Experience (QoE) of vehicles and this significantly affects the quality of communication  ...  The evaluations show that the proposed model provides a better flow satisfied and throughput by guaranteeing energy efficiency in SDN-based vehicular networks.  ...  Data Plane In data plane, RSUs are deployed along the road in order to serve the vehicles within the coverage area.  ... 
doi:10.4108/eai.9-1-2017.152099 fatcat:qwqb2c6qpff4rlrpmtvpwo5x6e

Projective clustering by histograms

E.K.K. Ng, A.W.-C. Fu, R.C.-W. Wong
2005 IEEE Transactions on Knowledge and Data Engineering  
Recent research suggests that clustering for high dimensional data should involve searching for "hidden" subspaces with lower dimensionalities, in which patterns can be observed when data objects are projected  ...  Our experiments compare behaviors and performances of this approach and other projective clustering algorithms with different data characteristics.  ...  Acknowledgements We thank Lai Mei Chiu for her help in the data generations and experiments. We thank the anonymous reviewers for their valuable comments and suggestions.  ... 
doi:10.1109/tkde.2005.47 fatcat:wsv24woa75fflhbr6urbjxrfri

Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI

L. Kerem Senel, Toygan Kilic, Alper Gungor, Emre Kopanoglu, H. Emre Guven, Emine U. Saritas, Aykut Koc, Tolga Cukur
2019 IEEE Transactions on Medical Imaging  
However, naive random patterns often contain gaps or clusters across the acquisition dimension that, in turn, can degrade reconstruction quality or reduce scan efficiency.  ...  Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo [Formula: see text]-weighted imaging.  ...  In turn, a k-space gap can impair the recovery of unacquired data, whereas a k-space cluster can reduce scan efficiency by collecting redundant information.  ... 
doi:10.1109/tmi.2019.2892378 pmid:30640604 fatcat:yxt32gnsxng4dhxbd55pqvp5tu

"e-Push or e-Pull? Laggards and First-Movers in European On-Line Banking

Jacques Bughin
2006 Journal of Computer-Mediated Communication  
Among others, banks with traditionally high cost-effectiveness and which already offer wide private ATM coverage for their customers are also the ones which have already started to migrate a larger proportion  ...  Finally, a cluster analysis supplements the regression results along the axes of "pull" and "push", and identifies a set of early movers and laggard among the banks in our sample-Generally speaking, those  ...  With this extensive density coverage, banks with strong policy of ATM development might also be the ones to pioneer on-line push to their customers.  ... 
doi:10.1111/j.1083-6101.2001.tb00133.x fatcat:3ccjxfywnrd7djklhexajm6kp4

Energy efficient data aggregation and improved prediction in cooperative surveillance system through Machine Learning and Particle Swarm based Optimization

Neethu John, Neena Joseph, Nimmymol Manuel, Sruthy Emmanuel, Simy Kurian
2021 EAI Endorsed Transactions on Energy Web  
The application of effective data aggregation technique on sensors is essential as the energy utilization of the system degrades the lifetime, coverage and computational overhead.  ...  Heterogeneous types of sensors are used to improve the effectiveness of this surveillance system and a cooperative approach of such sensors will make the system further efficient due to variation in users  ...  Energy efficient and coverage improved data aggregation and data analytics Particle swarm optimization (PSO) In order to further improve the coverage and network lifetime, Particle Swarm Optimization  ... 
doi:10.4108/eai.3-6-2021.170014 fatcat:6eqn7v5ta5egxdww56zdrfynze

A Multi-objective Fuzzy Logic based Multi-path Routing Algorithm for WSNs

G Spica Sujeetha, Assistant professor, Department of ECE, Narsimha Reddy Engineering College, Hyderabad, Telangana, India
2022 International Journal of Wireless and Microwave Technologies  
The clustering technique is the reliable data gathering algorithm for achieving energy-efficiency.  ...  , energy efficiency and better data delivery.  ...  Efficient clustering algorithm with enhanced cohesive quality clusters.  ... 
doi:10.5815/ijwmt.2022.01.04 fatcat:72e55snwczhgvgqq225desrjmm

An Evaluation Research into Sustainable Development of Water Resources in Lakes Watershed

R. Li
2016 Chemical Engineering Transactions  
Grey clustering method was applied to analyzing whitenization weight function and grey clustering coefficients concerning evaluation indices of SWRDWLB, so that the comprehensive evaluation grade could  ...  The paper studied SWRDWLB by means of analyzing various influential factors, establishing an evaluation system, and determining evaluation index weights with the help of analytic hierarchy process as well  ...  weight function for the k subcategory of Index i , and i W denoted the weight of Index i in integral clustering classification.  ... 
doi:10.3303/cet1651165 doaj:6ae7ac770915474ca694771933b41e9a fatcat:4ai36gvkjncqtbdmp3abjjcaeu

Direct training of subspace distribution clustering hidden Markov model

B. Kan-Wing Mak, E. Bocchieri
2001 IEEE Transactions on Speech and Audio Processing  
With such compact acoustic models, one should be able to train SDCHMMs directly from significantly less speech data (without intermediate CDHMMs).  ...  Recently, we proposed a compact acoustic model called "subspace distribution clustering hidden Markov model" (SDCHMM) with an aim to save some of the training effort.  ...  It has been shown that the subspace distribution clustering hidden Markov models (SDCHMMs) can capture the acoustic-phonetic information efficiently with significantly fewer parameters-by one to two orders  ... 
doi:10.1109/89.917683 fatcat:l46iembbgfeyzb2rxbuqxd5hsa

Constraint-based Subspace Clustering [chapter]

Elisa Fromont, Adriana Prado, Celine Robardet
2009 Proceedings of the 2009 SIAM International Conference on Data Mining  
In high dimensional data, the general performance of traditional clustering algorithms decreases.  ...  We show how this new framework can be applied to both density and distancebased bottom-up subspace clustering techniques.  ...  Top-down subspace clustering techniques start with an initial approximation of the clusters in the full fea-ture space and iteratively refine the clustering by assigning a weight to each of the dimensions  ... 
doi:10.1137/1.9781611972795.3 dblp:conf/sdm/FromontPR09 fatcat:dyjob5bwjrdvtfffoscoqhad3a
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