Asccn

Wan, Renxia; Wang, Lixin; Su, Xiaoke

doi:10.4018/jdwm.2010100101

Cited by 5 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sampling approaches (Aggarwal et al, 2009;Cheng et al, 1998;Guha et al, 1998;Kranen et al, 2011;Lee et al, 2009;Ng et al, 2002;Pal et al, 2002;Sakai et al, 2009;Yildizli et al, 2011) usually choose the samples by a certain rule such as chisquare or divergence hypothesis (Hathaway et al, 2006). The incremental approaches (Bradley et al, 1998;Farnstrom et al, 2000;Gupta et al, 2004;Karkkainen et al, 2007;Luhr et al, 2009;Nguyen-Hoang et al, 2009;Ning et al, 2009;O'Callaghan et al, 2002;Ramakrishnan et al, 1996;Siddiqui et al, 2009;Wan et al, 2010Wan et al, , 2011 generally maintain past knowledge from the previous runs of a clustering algorithm to produce or improve the future clustering model. Nevertheless, as Hore et al (2007) pointed out, many existing algorithms for large and very large data sets are used for the crisp case, rarely for the fuzzy case.…”

Section: Introductionmentioning

confidence: 99%

Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets

Wan

Gao

2012

International Journal of Data Warehousing and Mining

Self Cite

View full text Add to dashboard Cite

Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes.

show abstract

Section: Introductionmentioning

confidence: 99%

Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets

Wan

Gao

2012

International Journal of Data Warehousing and Mining

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is an unsupervised technique without the knowledge what causes the grouping and how many groups exist (Song, Hu, & Yoo, 2009;Engle & Gangopadhyay, 2010;Silla & Freitas, 2011). The arbitrary shaped clustering was further treated (Wan, Wang, & Su, 2010). Clustering may be implemented on hierarchy, partition, density, grid, constraint, subspace and so on (Sander et al, 1998;Kwok et al, 2002;Grabmeier & Rudolph, 2002;Parsons, Haque, & Liu, 2004;Zhang et al, 2008;Horng et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Data Field for Hierarchical Clustering

Wang

Gan

et al. 2011

International Journal of Data Warehousing and Mining

View full text Add to dashboard Cite

In this paper, data field is proposed to group data objects via simulating their mutual interactions and opposite movements for hierarchical clustering. Enlightened by the field in physical space, data field to simulate nuclear field is presented to illuminate the interaction between objects in data space. In the data field, the self-organized process of equipotential lines on many data objects discovers their hierarchical clustering-characteristics. During the clustering process, a random sample is first generated to optimize the impact factor. The masses of data objects are then estimated to select core data object with nonzero masses. Taking the core data objects as the initial clusters, the clusters are iteratively merged hierarchy by hierarchy with good performance. The results of a case study show that the data field is capable of hierarchical clustering on objects varying size, shape or granularity without user-specified parameters, as well as considering the object features inside the clusters and removing the outliers from noisy data. The comparisons illustrate that the data field clustering performs better than K-means, BIRCH, CURE, and CHAMELEON.

show abstract

Generating Information-Rich High-Throughput Experimental Materials Genomes using Functional Clustering via Multitree Genetic Programming and Information Theory

et al. 2015

View full text Add to dashboard Cite

In this article, we propose a new Cauchy-Schwarz divergence function that is invariant to number of clusters. To illustrate the applicability of our approach, we assign random memberships for various number of clusters (2 to 8) to 486 compositions distributed at a 3.33 at.% interval in a ternary library; and plot the cross cluster information potential, self-information potential and their ratio using the Cauchy-Schwarz divergence function proposed by Boric et al. 1 (Eq. 1) and the Cauchy-Schwarz divergence function proposed in our article (Eq. 2) in Fig.1 and 2 respectively. From Fig. 1 we see that the self-information potential increases as a power of number of clusters (c); whereas the cross information potential increases very slowly as a function of c. Thus, the selfinformation potential dominates the Cauchy-Schwarz divergence function as the number of clusters increases.In Fig. 2; we show that the Cauchy-Schwarz divergence function proposed in our approach results in cross-information and self-information potentials that are invariant to number of clusters (c). Further, the ratio of cross-information potential to self-information potential is approximately 1 resulting in approximately zero Cauchy-Schwarz divergence; as expected for compositions with random memberships.

show abstract

Asccn

Cited by 5 publications

References 31 publications

Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets

Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets

Data Field for Hierarchical Clustering

Generating Information-Rich High-Throughput Experimental Materials Genomes using Functional Clustering via Multitree Genetic Programming and Information Theory

Contact Info

Product

Resources

About