QC4 - A Clustering Evaluation Method

Crabtree, Daniel; Andreae, Peter; Gao, Xiaoying

doi:10.1007/978-3-540-71701-0_9

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…Note that DS2 was obtained from Chameleon [11], whereas we synthetically generated the remaining data set. Precision, recall and F-measure are common measurements used in information retrieval for evaluation [12]. Their comparison of DBSCAN, OPTICS and CDDC is shown in figure 8.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Dispersive Degree based Clustering Algorithm Combined with Classification

Zhang¹,

Shan²,

Yu³

et al. 2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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The various-density problem has become one of the focuses in density based clustering research. A novel dispersive degree based algorithm combined with classification, called CDDC, is presented in this paper to remove the hurdle. In CDDC, a sequence is established for depicting the data distribution, discriminating cores and classifying edges. Clusters are discovered by utilizing the revealed information. Several experiments are performed and the results suggest that CDDC is effective in handling the various-density problem and is more efficient than the well-known algorithms such as DBSCAN, OPTICS and KNNCLUST.

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Section: Performance Evaluationmentioning

confidence: 99%

A Dispersive Degree based Clustering Algorithm Combined with Classification

Zhang¹,

Shan²,

Yu³

et al. 2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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“…There exists various clustering methods and approaches, like e.g. density-based, probabilistic, grid-based, and spectral clustering [2], together with their comparisons and evaluations (e.g., [6]). Although hierarchical methods allow summarization and exploration of a given dataset through the visual dendrogram, the basic form of the technique is not scalable to large number of observations because of the pairwise distance matrix requirement [25].…”

Section: Introductionmentioning

confidence: 99%

Feature Ranking of Large, Robust, and Weighted Clustering Result

Saarela

Hämäläinen

Kärkkäinen

2017

Advances in Knowledge Discovery and Data Mining

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Abstract. A clustering result needs to be interpreted and evaluated for knowledge discovery. When clustered data represents a sample from a population with known sample-to-population alignment weights, both the clustering and the evaluation techniques need to take this into account. The purpose of this article is to advance the automatic knowledge discovery from a robust clustering result on the population level. For this purpose, we derive a novel ranking method by generalizing the computation of the Kruskal-Wallis H test statistic from sample to population level with two different approaches. Application of these enlargements to both the input variables used in clustering and to metadata provides automatic determination of variable ranking that can be used to explain and distinguish the groups of population. The ranking method is illustrated with an open data and then, applied to advance the educational knowledge discovery from large scale international student assessment data, whose robust clustering into disjoint groups on three different levels of abstraction was performed in [19].

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“…First, the generated clusters have to be representative of the underlying graph, which implies that coverage of the clustering should be sufficiently high. Second, attribute purity [116] of the clusterings should correspond to the functional groups that were merged apriori. This can be determined through the purity of the molecule class attribute within the proteins in each cluster.…”

Section: Cluster Quality Comparisonmentioning

confidence: 99%

Multi-resolution functional summarization and alignment of biological network models

Seah¹

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I would like to thank my supervisor Assoc. Prof. Sourav S. Bhowmick (NTU) and my co-supervisor Prof. C. Forbes Dewey, Jr. (MIT) for their guidance and support. I have learned from them a multitude of skills, including research, writing and presentation skills. They have taught me that there is no substitute for professionalism and attention to detail. I also want to thank my colleagues Ms. Chua Huey Eng, Dr. Naveen

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QC4 - A Clustering Evaluation Method

Cited by 7 publications

References 6 publications

A Dispersive Degree based Clustering Algorithm Combined with Classification

A Dispersive Degree based Clustering Algorithm Combined with Classification

Feature Ranking of Large, Robust, and Weighted Clustering Result

Multi-resolution functional summarization and alignment of biological network models

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