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

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.

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.

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

Approximating Dunn’s Cluster Validity Indices for Partitions of Big Data

2019 ◽  
Vol 49 (5) ◽  
pp. 1629-1641 ◽  
Author(s):  
Punit Rathore ◽  
Zahra Ghafoori ◽  
James C. Bezdek ◽  
Marimuthu Palaniswami ◽  
Christopher Leckie
Keyword(s):  
Big Data ◽  
Cluster Validity ◽  
Cluster Validity Indices ◽  
Validity Indices

scholarly journals A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters

2017 ◽  
Vol 65 (4) ◽  
pp. 359-365 ◽  
Author(s):  
Javier Senent-Aparicio ◽  
Jesús Soto ◽  
Julio Pérez-Sánchez ◽  
Jorge Garrido
Keyword(s):  
Frequency Analysis ◽  
Fuzzy Clustering ◽  
Optimal Number ◽  
Regional Frequency Analysis ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Homogeneous Regions ◽  
Optimal Number Of Clusters

AbstractOne of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis.

scholarly journals Assessment of some combinations of hard and fuzzy clustering techniques for regionalisation of catchments in Sefidroud basin

2016 ◽  
Vol 18 (6) ◽  
pp. 1033-1054 ◽  
Author(s):  
Ali Ahani ◽  
S. Saeid Mousavi Nadoushani
Keyword(s):  
Objective Function ◽  
Clustering Algorithms ◽  
Hybrid Approach ◽  
Hybrid Algorithms ◽  
Flood Frequency Analysis ◽  
Cluster Validity ◽  
Fcm Algorithm ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Regional Flood Frequency Analysis

Cluster analysis methods are a type of well-known technique for regionalisation of catchments to perform regional flood frequency analysis. In this study, a fuzzy extension of hybrid clustering algorithms is evaluated. Self-organizing feature maps and four hierarchical clustering algorithms were used to provide the initial cluster centres for fuzzy c-means (FCM) algorithm. The hybrid approach was used for regionalisation of catchments in Sefidroud basin based on feature vectors including five catchment attributes: longitude and latitude, drainage area, runoff coefficient and mean annual precipitation. The results showed that according to the values of both the objective function and the cluster validity indices, the performances of FCM algorithm often was improved by using the proposed hybrid approach. Also, it was evident from the results that in the case of minimizing the objective function, the combination of Ward's algorithm and FCM provided best results, but according to the cluster validity indices, other hybrid algorithms such as combinations of single linkage or complete linkage and FCM algorithm presented the most desirable results. In addition, according to the results, there are two well-defined homogeneous regions in Sefidroud basin identified by all the examined hybrid algorithms.

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