An Algebra Description for Hard Clustering

2015 ◽  
pp. 62-69
Author(s):  
Bo Wang ◽  
Yong Shi ◽  
Zhuofan Yang ◽  
Xuchan Ju
Keyword(s):  
Hard Clustering

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

A New Clustering Method with Fuzzy Approach Based on Takagi-Sugeno Model in Queuing Systems

2013 ◽  
Vol 3 (2) ◽  
pp. 32-54
Author(s):  
Farzaneh Gholami Zanjanbar ◽  
Inci Sentarli
Keyword(s):  
Fuzzy Model ◽  
Extraction Process ◽  
Queuing System ◽  
Rule Base ◽  
Queuing Systems ◽  
Clustering Method ◽  
Hard Clustering ◽  
Sugeno Model ◽  
Takagi Sugeno ◽  
Linear Controllers

In this paper, the authors propose a new hard clustering method to provide objective knowledge on field of fuzzy queuing system. In this method, locally linear controllers are extracted and translated into the first-order Takagi-Sugeno rule base fuzzy model. In this extraction process, the region of fuzzy subspaces of available inputs corresponding to different implications is used to obtain the clusters of outputs of the queuing system. Then, the multiple regression functions associated with these separate clusters are used to interpret the performance of queuing systems. An application of the method also is presented and the performance of the queuing system is discussed.

Business Process Model Abstraction Based on Fuzzy Clustering Analysis

2019 ◽  
Vol 28 (03) ◽  
pp. 1950007
Author(s):  
Nan Wang ◽  
Shanwu Sun ◽  
Ying Liu ◽  
Senyue Zhang
Keyword(s):  
Business Process ◽  
Fuzzy Clustering ◽  
Process Model ◽  
Control Flow ◽  
Business Process Model ◽  
Model Abstraction ◽  
Fuzzy Matrix ◽  
Fuzzy Clustering Analysis ◽  
Complex Process ◽  
Hard Clustering

The most prominent Business Process Model Abstraction (BPMA) use case is a construction of a process “quick view” for rapidly comprehending a complex process. Researchers propose various process abstraction methods to aggregate the activities most of which are based on [Formula: see text]-means hard clustering. This paper focuses on the limitation of hard clustering, i.e. it cannot identify the special activities (called “edge activities” in this paper) and each activity must be classified to some subprocess. A new method is proposed to classify activities based on fuzzy clustering which generates a fuzzy matrix by computing the possibilities of activities belonging to subprocesses. According to this matrix, the “edge activities” can be located. Considering the structure correlation feature of the activities in subprocesses, an approach is provided to generate the initial clusters based on the close connection characteristics of subprocesses. A hard partition algorithm is proposed to classify the edge activities and it evaluates the generated abstract models according to a new index designed by control flow order preserving requirement and the evaluation results guide the edge activities to be classified to the optimal hard partition. The proposed method is applied to a process model repository in use. The results verify the validity of the measurement based on the virtual document to generating fuzzy matrix. Also it mines the threshold parameter in the real world process model collection enriched with human designed subprocesses to compute the fuzzy matrix. Furthermore, a comparison is made between the proposed method and the [Formula: see text]-means clustering and the results show our approach more closely approximating the decisions of the involved modelers to cluster activities and it contributes to the development of modeling support for effective process model abstraction.

Soft and hard clustering methods for delineation of hydrological homogeneous regions in the southern strip of the Caspian Sea Watershed

2012 ◽  
Vol 5 (4) ◽  
pp. 282-294 ◽  
Author(s):  
S. Chavoshi ◽  
W.N. Azmin Sulaiman ◽  
B. Saghafian ◽  
MD. N.B. Sulaiman ◽  
A.M. Latifah
Keyword(s):  
Caspian Sea ◽  
Clustering Methods ◽  
The Caspian Sea ◽  
Hard Clustering ◽  
Homogeneous Regions

scholarly journals Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction

2019 ◽  
Vol 45 (3) ◽  
pp. 423-479
Author(s):  
Dmitry Ustalov ◽  
Alexander Panchenko ◽  
Chris Biemann ◽  
Simone Paolo Ponzetto
Keyword(s):  
Computational Complexity ◽  
Computational Analysis ◽  
Graph Clustering ◽  
Intermediate Representation ◽  
Fuzzy Graph ◽  
Input Graph ◽  
Semantic Class ◽  
Linguistic Data ◽  
Hard Clustering ◽  
Intermediate Graph

We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains. This algorithm creates an intermediate representation of the input graph, which reflects the “ambiguity” of its nodes. Then, it uses hard clustering to discover clusters in this “disambiguated” intermediate graph. After outlining the approach and analyzing its computational complexity, we demonstrate that Watset shows competitive results in three applications: unsupervised synset induction from a synonymy graph, unsupervised semantic frame induction from dependency triples, and unsupervised semantic class induction from a distributional thesaurus. Our algorithm is generic and can also be applied to other networks of linguistic data.

scholarly journals Semi-hard Clustering with Application to Classifier Design

2010 ◽  
Vol 22 (3) ◽  
pp. 358-367 ◽  
Author(s):  
Hidetomo ICHIHASHI ◽  
Akira NOTSU ◽  
Katsuhiro HONDA
Keyword(s):  
Classifier Design ◽  
Hard Clustering

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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