A Comparative Study of Hard Clustering Algorithms for Vegetation Data

2021 ◽  
Author(s):  
Naghmeh Pakgohar ◽  
Javad Eshaghi Rad ◽  
Gholam Hossein Gholami ◽  
Ahmad Alijanpour ◽  
David W. Roberts
Keyword(s):  
Comparative Study ◽  
Clustering Algorithms ◽  
Hard Clustering

Data Clustering Algorithms Using Rough Sets

2013 ◽  
pp. 297-327 ◽  
Author(s):  
B.K. Tripathy ◽  
Adhir Ghosh
Keyword(s):  
Comparative Study ◽  
Rough Set ◽  
Fuzzy Clustering ◽  
Fuzzy Set ◽  
Rough Sets ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Future Studies ◽  
Multiple Clusters

Developing Data Clustering algorithms have been pursued by researchers since the introduction of k-means algorithm (Macqueen 1967; Lloyd 1982). These algorithms were subsequently modified to handle categorical data. In order to handle the situations where objects can have memberships in multiple clusters, fuzzy clustering and rough clustering methods were introduced (Lingras et al 2003, 2004a). There are many extensions of these initial algorithms (Lingras et al 2004b; Lingras 2007; Mitra 2004; Peters 2006, 2007). The MMR algorithm (Parmar et al 2007), its extensions (Tripathy et al 2009, 2011a, 2011b) and the MADE algorithm (Herawan et al 2010) use rough set techniques for clustering. In this chapter, the authors focus on rough set based clustering algorithms and provide a comparative study of all the fuzzy set based and rough set based clustering algorithms in terms of their efficiency. They also present problems for future studies in the direction of the topics covered.

scholarly journals A Comparative Study of Data Clustering Algorithms

2013 ◽  
Vol 83 (15) ◽  
pp. 41-46 ◽  
Author(s):  
Geet Singhal ◽  
Shipra Panwar ◽  
Kanika Jain ◽  
Devender Banga
Keyword(s):  
Comparative Study ◽  
Data Clustering ◽  
Clustering Algorithms

scholarly journals Comparative Study of Clustering Algorithms Used in Counter Terrorism

2014 ◽  
Vol 16 (6) ◽  
pp. 13-17
Author(s):  
Sanjay Dwivedi ◽  
◽  
Prabhat Pandey ◽  
Manmohan Singh Tiwari ◽  
Mohd. Athar Kalam
Keyword(s):  
Comparative Study ◽  
Clustering Algorithms ◽  
Counter Terrorism

scholarly journals A comparative analysis of selected clustering algorithms for criminal profiling

2020 ◽  
Vol 39 (2) ◽  
pp. 464-471
Author(s):  
J.A. Adeyiga ◽  
S.O. Olabiyisi ◽  
E.O. Omidiora
Keyword(s):  
Expectation Maximization ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Expectation Maximization Algorithm ◽  
Real Life ◽  
Criminal Activity ◽  
Law Enforcement Agencies ◽  
Criminal Profiling ◽  
Hard Clustering ◽  
Membership Value

Several criminal profiling systems have been developed to assist the Law Enforcement Agencies in solving crimes but the techniques employed in most of the systems lack the ability to cluster criminal based on their behavioral characteristics. This paper reviewed different clustering techniques used in criminal profiling and then selects one fuzzy clustering algorithm (Expectation Maximization) and two hard clustering algorithm (K-means and Hierarchical). The selected algorithms were then developed and tested on real life data to produce "profiles" of criminal activity and behavior of criminals. The algorithms were implemented using WEKA software package. The performance of the algorithms was evaluated using cluster accuracy and time complexity. The results show that Expectation Maximization algorithm gave a 90.5% clusters accuracy in 8.5s, while K-Means had 62.6% in 0.09s and Hierarchical with 51.9% in 0.11s. In conclusion, soft clustering algorithm performs better than hard clustering algorithm in analyzing criminal data. Keywords: Clustering Algorithm, Profiling, Crime, Membership value

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