Fast and Robust General Purpose Clustering Algorithms

Estivill‐Castro, Vladimir; Yang, Jianhua

doi:10.1023/b:dami.0000015869.08323.b3

Cited by 68 publications

(35 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PAM utilizes real data points (medoids) as the cluster prototypes and avoids the effect of outliers. Based on the same consideration, a -medoids algorithm is presented in [87] by searching the discrete 1-medians as the cluster centroids.…”

Section: )mentioning

confidence: 99%

Survey of Clustering Algorithms

Wunsch

2005

IEEE Trans. Neural Netw.

5,056

2,338

View full text Add to dashboard Cite

Abstract-Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.Index Terms-Adaptive resonance theory (ART), clustering, clustering algorithm, cluster validation, neural networks, proximity, self-organizing feature map (SOFM).

show abstract

Section: )mentioning

confidence: 99%

Survey of Clustering Algorithms

Wunsch

2005

IEEE Trans. Neural Netw.

5,056

2,338

View full text Add to dashboard Cite

show abstract

“…In Ref. [37], a K-medoids algorithm is presented. In this cluster centroid is searched using the discrete 1-medians.…”

Section: Algorithmmentioning

confidence: 99%

“…In paper [29], [38] and [39] different variants of K-means algorithm has been used which can be applied to categorical data. The Proposed K-medoids [37] and K-modes algorithm operates in a similar way as K -means.…”

Section: Algorithmmentioning

confidence: 99%

Gene Expression Analysis Using Clustering

Kumar¹,

Rath²

2009

IJCEE

View full text Add to dashboard Cite

Abstract-Data Mining has become an important topic in effective analysis of gene expression data due to its wide application in the biomedical industry. In this paper, k-means clustering algorithm has been extensively studied for gene expression analysis. Since our purpose is to demonstrate the effectiveness of the k-means algorithm for a wide variety of data sets, Two pattern recognition data and thirteen microarray data sets with both overlapping and non-overlapping class boundaries were taken for studies, where the number of features/genes ranges from 4 to 7129 and number of sample ranges from 32 to 683. The number of clusters ranges from two to eleven.

show abstract

“…On the other hand, clustering of pages will discover groups of pages having related content. Several researchers have applied data mining techniques to web server logs, attempting to unlock the usage patterns of web users hidden in the log files [13].…”

Section: Web User Clusteringmentioning

confidence: 99%

Mining and Analysis of Clickstream Patterns

Inbarani

Thangavel

2009

Studies in Computational Intelligence

View full text Add to dashboard Cite

Abstract. The explosive growth of the web has drastically changed the way in which information is managed and accessed. The large-scale of web data sources and the wide availability of services over the internet have increased the need for effective web data mining techniques and mechanisms . A sophisticated method to organize the layout of the information and assist user navigation is therefore particularly important. In this work, we focus on web usage mining, applying data mining techniques to web server logs. Web usage mining is the non-trivial process of distinguishing implicit, previously unknown but potentially useful clickstream patterns that may exist in any collection of web access logs. The required abstraction can be generated by clustering the web access logs based on some sort of similarity measure. Clustering is done such that the web access logs within the same group or cluster are more similar than data points from different clusters. In this chapter, we propose a partitional algorithm namely Multi Pass Combined Standard Deviation(CSD) Means algorithm which automatically generates the optimum number of clusters from the web clickstream patterns. The quality of clusters obtained using these algorithms are compared using K-Means algorithm, Rough K-Means algorithm and model based algorithms ANTCLUST and ACCANTCLUST. The experimental analysis of mined clickstream patterns shows the effectiveness of the proposed algorithm.

show abstract

Fast and Robust General Purpose Clustering Algorithms

Cited by 68 publications

References 34 publications

Survey of Clustering Algorithms

Survey of Clustering Algorithms

Gene Expression Analysis Using Clustering

Mining and Analysis of Clickstream Patterns

Contact Info

Product

Resources

About