scholarly journals Online cluster validity indices for performance monitoring of streaming data clustering

2018 ◽  
Vol 34 (4) ◽  
pp. 541-563 ◽  
Author(s):  
Masud Moshtaghi ◽  
James C. Bezdek ◽  
Sarah M. Erfani ◽  
Christopher Leckie ◽  
James Bailey
Keyword(s):  
Data Clustering ◽  
Performance Monitoring ◽  
Streaming Data ◽  
Cluster Validity ◽  
Cluster Validity Indices ◽  
Validity Indices

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

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.

Performance Evaluation of the Data Clustering Techniques and Cluster Validity Indices for Efficient Toolpath Development for Incremental Sheet Forming

2020 ◽  
pp. 1-32
Author(s):  
Aniket Nagargoje ◽  
Pavan K. Kankar ◽  
Prashant K. Jain ◽  
Puneet Tandon
Keyword(s):  
Hierarchical Clustering ◽  
Data Clustering ◽  
Spectral Clustering ◽  
Sheet Forming ◽  
Incremental Sheet Forming ◽  
Cluster Validity ◽  
Clustering Techniques ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Feature Based

Abstract The goal of this research is to compare the data clustering techniques and cluster validity indices for feature-based tool path development, in case of incremental sheet forming process. The work compares the four most popular clustering techniques, i.e., partition-based (K-means), density-based (DBSCAN), variants of hierarchical clustering and graph-based (Spectral) clustering technique. Besides, for the quality assessment of the clustering solutions and to pinpoint the superlative validity indices, techniques like Calinski-Harabasz, Ball-Hall, Davies-Bouldin, Dunn, Det Ratio, Silhouette, Trace WiB, and Log Det Ratio are compared. The Single Linkage Hierarchical Clustering is preferred over the other variants as it detects the arbitrarily shaped clusters. After comparing it with DBSCAN, K-means, and Spectral clustering, it is found that DBSCAN is the best suitable technique for the proposed application. From the comparison of the internal validity indices, the following four out of eight techniques, Ball-Hall, Dunn, Det Ratio, Log Det Ratio indices are selected as they support the application. The outcome of this research would help in building algorithms for feature-based toolpath development strategies for manufacturing industry using data science and machine learning techniques.

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

2014 ◽  
Vol 37 (1) ◽  
pp. 141-157 ◽  
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.

scholarly journals Incremental Cluster Validity Indices for Online Learning of Hard Partitions: Extensions and Comparative Study

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 22025-22047 ◽  
Author(s):  
Leonardo Enzo Brito Da Silva ◽  
Niklas Max Melton ◽  
Donald C. Wunsch
Keyword(s):  
Online Learning ◽  
Comparative Study ◽  
Cluster Validity ◽  
Cluster Validity Indices ◽  
Validity Indices

scholarly journals Towards a standard methodology to evaluate internal cluster validity indices

2011 ◽  
Vol 32 (3) ◽  
pp. 505-515 ◽  
Author(s):  
Ibai Gurrutxaga ◽  
Javier Muguerza ◽  
Olatz Arbelaitz ◽  
Jesús M. Pérez ◽  
José I. Martín
Keyword(s):  
Cluster Validity ◽  
Standard Methodology ◽  
Cluster Validity Indices ◽  
Validity Indices
Sign in / Sign up

Export Citation Format

Share Document