A New Clustering Algorithm Based On Cluster Validity Indices

The delineation of precipitation regions is to identify homogeneous zones in which the characteristics of the process are statistically similar. The regionalization process has three main components: (i) delineation of regions using clustering algorithms, (ii) determining the optimal number of regions using cluster validity indices (CVIs), and (iii) validation of regions for homogeneity using L-moments ratio test. The identification of the optimal number of clusters will significantly affect the homogeneity of the regions. The objective of this study is to investigate the performance of the various CVIs in identifying the optimal number of clusters, which maximizes the homogeneity of the precipitation regions. The k-means clustering algorithm is adopted to delineate the regions using location-based attributes for two large areas from Canada, namely, the Prairies and the Great Lakes-St Lawrence lowlands (GL-SL) region. The seasonal precipitation data for 55 years (1951–2005) is derived using high-resolution ANUSPLIN gridded point data for Canada. The results indicate that the optimal number of clusters and the regional homogeneity depends on the CVI adopted. Among 42 cluster indices considered, 15 of them outperform in identifying the homogeneous precipitation regions. The Dunn, D e t _ r a t i o and Trace( W − 1 B ) indices found to be the best for all seasons in both the regions.

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A Comparison Study of Cluster Validity Indices Using a Nonhierarchical Clustering Algorithm

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06) ◽

10.1109/cimca.2005.1631265 ◽

2006 ◽

Cited By ~ 1

Author(s):

Yosung Shim ◽

Jiwon Chung ◽

In-Chan Choi

Keyword(s):

Clustering Algorithm ◽

Comparison Study ◽

Cluster Validity ◽

Cluster Validity Indices ◽

Validity Indices ◽

Nonhierarchical Clustering

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AutoClust: A Framework for Automated Clustering Based on Cluster Validity Indices

2020 IEEE International Conference on Data Mining (ICDM) ◽

10.1109/icdm50108.2020.00153 ◽

2020 ◽

Author(s):

Yannis Poulakis ◽

Christos Doulkeridis ◽

Dimosthenis Kyriazis

Keyword(s):

Cluster Validity ◽

Cluster Validity Indices ◽

Validity Indices

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On fuzzy cluster validity indices for the objects of mixed features

2009 IEEE International Conference on Fuzzy Systems ◽

10.1109/fuzzy.2009.5277190 ◽

2009 ◽

Cited By ~ 2

Author(s):

Mahnhoon Lee

Keyword(s):

Fuzzy Cluster ◽

Cluster Validity ◽

Cluster Validity Indices ◽

Validity Indices ◽

Mixed Features

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Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM

Studies in Logic, Grammar and Rhetoric ◽

10.2478/slgr-2014-0022 ◽

2014 ◽

Vol 37 (1) ◽

pp. 141-157 ◽

Cited By ~ 1

Author(s):

Mariusz Łapczyński ◽

Bartłomiej Jefmański

Keyword(s):

Predictive Models ◽

Cluster Validity ◽

Number Of Clusters ◽

Model Combining ◽

Cluster Validity Indices ◽

Validity Indices ◽

And Cluster Analysis ◽

Analytical Tools ◽

F Measure

Abstract Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company (acquisition models), customers who purchase additional products (cross- and up-sell models) or customers intending to resign from the cooperation (churn models). During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive model combining decision trees (C&RT algorithm) and cluster analysis (k-means). During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality.

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Assessment of Twitter Data Clusters with Cosine-Based Validation Metrics Using Hybrid Topic Models

Ingénierie des systèmes d information ◽

10.18280/isi.250606 ◽

2020 ◽

Vol 25 (6) ◽

pp. 755-769

Author(s):

Noorullah R. Mohammed ◽

Moulana Mohammed

Keyword(s):

Data Clustering ◽

Topic Models ◽

Cluster Validity ◽

Text Documents ◽

Text Data ◽

Validity Assessment ◽

Text Document ◽

Cluster Validity Indices ◽

Validity Indices ◽

Data Clusters

Text data clustering is performed for organizing the set of text documents into the desired number of coherent and meaningful sub-clusters. Modeling the text documents in terms of topics derivations is a vital task in text data clustering. Each tweet is considered as a text document, and various topic models perform modeling of tweets. In existing topic models, the clustering tendency of tweets is assessed initially based on Euclidean dissimilarity features. Cosine metric is more suitable for more informative assessment, especially of text clustering. Thus, this paper develops a novel cosine based external and interval validity assessment of cluster tendency for improving the computational efficiency of tweets data clustering. In the experimental, tweets data clustering results are evaluated using cluster validity indices measures. Experimentally proved that cosine based internal and external validity metrics outperforms the other using benchmarked and Twitter-based datasets.

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