A new validity index for evaluating the clustering results by partitional clustering algorithms

Yue, Shihong; Wang, Jianpei; Wang, Jeen-Shing; Bao, Xiaojun

doi:10.1007/s00500-014-1577-1

Cited by 25 publications

(11 citation statements)

References 21 publications

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“…For example, paper [14] presents a new validity index for crisp clustering, which emphasizes the cluster shape by using a high order characterization of its probability. In turn, to represent the separation among clusters a new measure called dual center is proposed in [27]. A new measure of connectivity is presented in [24].…”

Section: Introductionmentioning

confidence: 99%

A new validity index for crisp clusters

Starczewski

2015

Pattern Anal Applic

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In this paper, a new cluster validity index which can be considered as a measure of the accuracy of the partitioning of data sets is proposed. The new index, called the STR index, is defined as the product of two components which determine changes of compactness and separability of clusters during a clustering process. The maximum value of this index identifies the best clustering scheme. Three popular algorithms have been applied as underlying clustering techniques, namely complete-linkage, expectation maximization and K-means algorithms. The performance of the new index is demonstrated for several artificial and real-life data sets. Moreover, this new index has been compared with other well-known indices, i.e., Dunn, Davies-Bouldin, PBM and Silhouette indices, taking into account the number of clusters in a data set as the comparison criterion. The results prove superiority of the new index as compared to the above-mentioned indices.

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Section: Introductionmentioning

confidence: 99%

A new validity index for crisp clusters

Starczewski

2015

Pattern Anal Applic

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“…By minimizing φ c and maximizing δ c , (4) can reach its maximum or minimum. Most validity indices take a trial-anderror way to find the optimum of (4) [28]. Various combinations of δ c and φ c can result in different kinds of indices [29].…”

Section: Related Workmentioning

confidence: 99%

Volume and Surface Area-Based Cluster Validity Index

Yue

Ding

2020

IEEE Access

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Cluster validity index plays an important role in assessing the quality of clustering results. However, most of the existing validity indices take a trial-and-error strategy, and their correctness depend on not only the measurements of intra-and inter-cluster distances but also the specific clustering algorithms and data structures. Consequently, the applications of these indices are limited in practice. In this paper, we firstly define the total surface area and volume of all clusters in a 2-dimensinal data space, thereby recovering their natural interrelation among various numbers of clusters. On this basis, a novel validity index is proposed to directly assess the clustering results of any dataset, which does not require any trail-and-error process, clustering algorithms, data structures, or the measurements of intra-and inter-cluster distances. In the case of a high-dimensional data space, all clusters are transformed into spherical clusters of normalized size in a 2-dimensinal data space through a multidimensional scaling transformation. Two groups of typical synthetic datasets and real datasets with various characteristics are used to validate the novel validity index.INDEX TERMS Cluster validity index, multidimensional scaling transformation, volume and surface area.

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“…For any clustering center v i determined by a partitional clustering algorithm, assume . v i is the closest prototype to v i , then the dual center is calculated as [33] can be constructed, i.e.,…”

Section: Davies-bouldin (Db) Indexmentioning

confidence: 99%

Boundary Matching and Interior Connectivity-Based Cluster Validity Anlysis

Yue

Wang

et al. 2020

Applied Sciences

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The evaluation of clustering results plays an important role in clustering analysis. However, the existing validity indices are limited to a specific clustering algorithm, clustering parameter, and assumption in practice. In this paper, we propose a novel validity index to solve the above problems based on two complementary measures: boundary points matching and interior points connectivity. Firstly, when any clustering algorithm is performed on a dataset, we extract all boundary points for the dataset and its partitioned clusters using a nonparametric metric. The measure of boundary points matching is computed. Secondly, the interior points connectivity of both the dataset and all the partitioned clusters are measured. The proposed validity index can evaluate different clustering results on the dataset obtained from different clustering algorithms, which cannot be evaluated by the existing validity indices at all. Experimental results demonstrate that the proposed validity index can evaluate clustering results obtained by using an arbitrary clustering algorithm and find the optimal clustering parameters.

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A new validity index for evaluating the clustering results by partitional clustering algorithms

Cited by 25 publications

References 21 publications

A new validity index for crisp clusters

A new validity index for crisp clusters

Volume and Surface Area-Based Cluster Validity Index

Boundary Matching and Interior Connectivity-Based Cluster Validity Anlysis

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