CLIQUE: Clustering based on density on web usage data: Experiments and test results

Santhisree, K.; Damodaram, A.

doi:10.1109/icectech.2011.5941893

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Ignores all projections of dimensional subspaces. [39,40] According to the reviewed clustering techniques in Table 2, this experimental work aims to investigate which category of clustering techniques would perform better in clustering genes. Gene expression data from the leukemia microarray study by Golub et al [49] are used in this study.…”

Section: Gene Network Clustering Techniquesmentioning

confidence: 99%

“…Grid-based clustering can benefit from dividing the data space into grids to reduce its time complexity [22,60]. CLIQUE, grid-clustering technique for high-dimensional very large spatial databases (GCHL), and statistical information grid (STING) are examples of grid-based clustering [39][40][41][42][43]. The GCHL technique can discover concave (deeper) and convex (higher) regions when applied in medical and geographical fields and by using the average eight direction (AED) technique [26,41].…”

Section: Category 3: Grid-based Clusteringmentioning

confidence: 99%

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A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

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Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

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Section: Gene Network Clustering Techniquesmentioning

confidence: 99%

Section: Category 3: Grid-based Clusteringmentioning

confidence: 99%

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

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“…On the basis of their approach, better session profiles were obtained by grouping similar sessions together when compared with those obtained using traditional association rules. Santhisree and Damodaram28 proposed the CLIQUE (CLUstering in QUEst) algorithm for clustering Web sessions for Web personalization. Various fuzzy similarity measures were used to measure the similarity of Web sessions using sequence alignment to determine learning behaviors.…”

Section: Fuzzy Web Usage Miningmentioning

confidence: 99%

A survey of fuzzy web mining

Lin

Hong

2013

WIREs Data Min & Knowl

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The Internet has become an unlimited resource of knowledge, and is thus widely used in many applications. Web mining plays an important role in discovering such knowledge. This mining can be roughly divided into three categories, including Web usage mining, Web content mining, and Web structure mining. Data and knowledge on the Web may, however, consist of imprecise, incomplete, and uncertain data. Because fuzzy‐set theory is often used to handle such data, several fuzzy Web‐mining techniques have been proposed to reveal fuzzy and linguistic knowledge. This paper reviews these techniques according to the three Web‐mining categories above—fuzzy Web usage mining, fuzzy Web content mining, and fuzzy Web structure mining. Some representative approaches in each category are introduced and compared. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Web Mining Technologies > Computational Intelligence

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“…CLIQUE is a clustering algorithm based on grid and density, which has the advantage of low time complexity and is suitable for the analysis of big data [1]. The CLIQUE algorithm is insensitive to incremental data and input order, and can find clusters of arbitrary shapes; it has good scalability for data sets of different scales, and it has good anti-interference performance for noisy data.…”

Section: Introductionmentioning

confidence: 99%

An Improved CLIQUE Algorithm

He¹,

Zhu³

2022

J. Phys.: Conf. Ser.

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CLIQUE is a clustering algorithm based on grid and density, which is sensitive to grid division parameter M and density threshold R, and has poor clustering accuracy. This paper proposes an improved algorithm, which is not sensitive to the initial grid division parameter M, and can automatically split the grid. Combined with the K-means algorithm, the clustering effect is improved. The F value on the S1 dataset is 36.9% higher than that of the classic CLIQUE algorithm.

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CLIQUE: Clustering based on density on web usage data: Experiments and test results

Cited by 7 publications

References 8 publications

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

A survey of fuzzy web mining

An Improved CLIQUE Algorithm

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