Evaluation measures for cluster ensembles based on a fuzzy generalized Rand index

Yeh, Chiou-Cherng; Yang, Miin-Shen

doi:10.1016/j.asoc.2017.03.030

Cited by 15 publications

(4 citation statements)

References 44 publications

(119 reference statements)

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“…The performance of competing methods is often evaluated by comparing labels obtained by the clustering method with the true labels provided in ground truth. In this paper, we evaluated our algorithm and all the comparison algorithms by utilizing four metrics: clustering accuracy, normalized mutual information, purity and adjusted rand index [4,10,22,28]. In the following we briefly introduce them.…”

Section: Evaluating Measuresmentioning

confidence: 99%

Jointly local statistics and topological proximity consideration in spectral clustering

Jamshidi

2023

Preprint

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Efficient deals of mathematical techniques have been devoted to cluster data with complicated manifold structures. Although local geometrical properties of data are considered in these techniques, some essential local statistics behaviours are ignored. In this paper, a method is proposed in which local topological and local statistics structures are jointly considered. More precisely, local correlations, local distributions and high-order proximity of data points are simultaneously considered to construct more efficient affinity matrix. Local correlations and high-order proximity are considered to modify the connectivity of the graph representation of data, and local distributions are utilized to obtain a free parameter similarity function between data points. After obtaining the new similarity matrix, spectral clustering is employed to evaluate the efficiency of it. The experimental results on various benchmark data sets demonstrate that the new method can significantly improve the clustering performance compared with state-of-the-art techniques.

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Section: Evaluating Measuresmentioning

confidence: 99%

Jointly local statistics and topological proximity consideration in spectral clustering

Jamshidi

2023

Preprint

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“…It quantifies the agreement between two partitions, i.e., how similar the clustering results are to the ground truth. The Rand index is defined as the following: [16,29,30].…”

Section: Rand Index (Ri)mentioning

confidence: 99%

“…The index value is 1 only if a partition is identical to the intrinsic structure, and it is close to 0 if the partition is random. ARI is utilized in a variety of research to evaluate data stream clusterings [16,29,30].…”

Section: Adjusted Rand Index (Ari)mentioning

confidence: 99%

A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

et al. 2022

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As applications generate massive amounts of data streams, the requirement for ways to analyze and cluster this data has become a critical field of research for knowledge discovery. Data stream clustering’s primary objective and goal are to acquire insights into incoming data. Recognizing all possible patterns in data streams that enter at variable rates and structures and evolve over time is critical for acquiring insights. Analyzing the data stream has been one of the vital research areas due to the inevitable evolving aspect of the data stream and its vast application domains. Existing algorithms for handling data stream clustering consider adding various data summarization structures starting from grid projection and ending with buffers of Core-Micro and Macro clusters. However, it is found that the static assumption of the data summarization impacts the quality of clustering. To fill this gap, an online clustering algorithm for handling evolving data streams using a tempo-spatial hyper cube called BOCEDS TSHC has been developed in this research. The role of the tempo-spatial hyper cube (TSHC) is to add more dimensions to the data summarization for more degree of freedom. TSHC when added to Buffer-based Online Clustering for Evolving Data Stream (BOCEDS) results in a superior evolving data stream clustering algorithm. Evaluation based on both the real world and synthetic datasets has proven the superiority of the developed BOCEDS TSHC clustering algorithm over the baseline algorithms with respect to most of the clustering metrics.

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“…Rand index is the method for performing an external evaluation of a clustering [15]. Rand index is a way to measure the percentage of decisions that are appropriate to the cluster result obtained, where rand index can be obtained by equation (8) [16].…”

Section: E Rand Indexmentioning

confidence: 99%

The Comparative Study on Clustering Method Using Hospital Facility Data in Jakarta District and Surrounding Areas

Romadon¹,

Fitrianah²

2019

IJMLC

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The present study reports the findings of a comparative analysis conducted on health facility research (HFR) data, based on a clustering method with K-Means and K-Medoids algorithm. The K-Means algorithm consist of four steps: specifyng the centroid values, grouping data on the centroid, calculating centroid values, repeating grouping data on the centroid, and calculating the values until the cluster is stable (convergence), The K-Medoids algorithm consists of six steps: medoids initialization, data allocation to the nearest medoid, determinination of new medoids, calculation of data distance with medoid, calculation of deviation, and repeating the determination of new medoids, until the deviation to the convergence cluster is counted. The data utilized in this study are HFR data located in the Jakarta district and surrounding areas, from 2013 to 2018. Our results showed that the execution time from K-Medoids algorithm outperformed the K-Means algorithm. The K-Medoids algorithm speeded up the execution time, and at the same time improved the density value between clusters (silhouette). By utilizing the clustering method, health facilities in hospitals in Jabodetabek can be categorized based on their resources.

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Evaluation measures for cluster ensembles based on a fuzzy generalized Rand index

Cited by 15 publications

References 44 publications

Jointly local statistics and topological proximity consideration in spectral clustering

Jointly local statistics and topological proximity consideration in spectral clustering

A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

The Comparative Study on Clustering Method Using Hospital Facility Data in Jakarta District and Surrounding Areas

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