Data Clustering and Various Clustering Approaches

2017 ◽  
pp. 90-108 ◽  
Author(s):  
Shashi Mehrotra ◽  
Shruti Kohli
Keyword(s):  
Hierarchical Clustering ◽  
Fuzzy Clustering ◽  
Data Clustering ◽  
Clustering Method ◽  
Extension Method ◽  
Advantages And Disadvantages ◽  
Density Based Clustering ◽  
Partition Clustering ◽  
Grid Based

It is needed to organize the data in different groups for various purposes, where clustering is useful. The chapter covers Data Clustering in the detail, which includes; introduction to data clustering with figures, data clustering process, basic classification of clustering and applications of clustering, describing hard partition clustering and fuzzy clustering. Some most commonly used clustering method are explained in the chapter with their features, advantages, and disadvantages. A various variant of K-Means and extension method of hierarchical clustering method, density-based clustering method and grid-based clustering method are covered.

Data Clustering

2009 ◽  
pp. 562-572
Author(s):  
Yanchang Zhao ◽  
Longbing Cao ◽  
Huaifeng Zhang ◽  
Chengqi Zhang
Keyword(s):  
Hierarchical Clustering ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Future Trends ◽  
Clustering Techniques ◽  
Density Based Clustering ◽  
Data Stream Clustering ◽  
Semisupervised Clustering ◽  
Definition Of ◽  
Grid Based

Clustering is one of the most important techniques in data mining. This chapter presents a survey of popular approaches for data clustering, including well-known clustering techniques, such as partitioning clustering, hierarchical clustering, density-based clustering and grid-based clustering, and recent advances in clustering, such as subspace clustering, text clustering and data stream clustering. The major challenges and future trends of data clustering will also be introduced in this chapter. The remainder of this chapter is organized as follows. The background of data clustering will be introduced in Section 2, including the definition of clustering, categories of clustering techniques, features of good clustering algorithms, and the validation of clustering. Section 3 will present main approaches for clustering, which range from the classic partitioning and hierarchical clustering to recent approaches of bi-clustering and semisupervised clustering. Challenges and future trends will be discussed in Section 4, followed by the conclusions in the last section.

scholarly journals FUZZY CLUSTERING MEANS (FCM) DALAM PENENTUAN LOKASI PENERTIBAN PENYAKIT MASYARAKAT PADA KEGIATAN PEMBINAAN SOSIAL SATPOL-PP WILAYAH SUMATRA-BARAT

Petir ◽  
2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Fuzzy Clustering ◽  
Community Policing ◽  
Sex Workers ◽  
Data Clustering ◽  
Behavior Patterns ◽  
Clustering Method ◽  
Street Vendors ◽  
West Sumatra ◽  
Municipal Police ◽  
Disease Community

Based on the data summary disease community policing activities by municipal police pp city of West Sumatra in January 2010 to December 2014, there were as many as 1660 cases of approximately 20 locations enforcement. Each location policing there are various types of activities are classified as a disease of society. Based on data obtained are activities that have curbed such as street vendors, illegal buildings, street children, street, commercial sex workers (CSWs) and others. Number of activities at each point different locations each year, thus requiring data clustering method to facilitate the investigation team in determining the behavior patterns of disease activity as a description of the location community policing a priority next year. The method used in this data clustering method is to use Fuzzy Clustering Means (FCM)

Ontology-Based K-Means Clustering Algorithm Analysis

2013 ◽  
Vol 380-384 ◽  
pp. 1290-1293
Author(s):  
Qing Ju Guo ◽  
Wen Tian Ji ◽  
Sheng Zhong
Keyword(s):  
Semantic Web ◽  
Clustering Algorithm ◽  
Algorithm Analysis ◽  
Clustering Method ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Research Findings ◽  
Partition Clustering ◽  
Improved Algorithm

Lots of research findings have been made from home and abroad on clustering algorithm in recent years. In view of the traditional partition clustering method K-means algorithm, this paper, after analyzing its advantages and disadvantages, combines it with ontology-based data set to establish a semantic web model. It improves the existing clustering algorithm in various constraint conditions with the aim of demonstrating that the improved algorithm has better efficiency and accuracy under semantic web.

scholarly journals Classification of Countries According to their Tourism Statistics via Different Cluster Analysis Methods and the Place of Turkey in this Structure

2013 ◽  
Author(s):  
Selay Giray
Keyword(s):  
Cluster Analysis ◽  
Hong Kong ◽  
Russian Federation ◽  
Fuzzy Clustering ◽  
Clustering Methods ◽  
Clustering Method ◽  
Analysis Methods ◽  
Fuzzy Clustering Method ◽  
The World

The aim of this study is to classify the countries according to their tourism indicators via different cluster analysis methods and compare the findings. Using classical cluster analysis and fuzzy clustering together will be more appropriate to determine the World tourism structure. In this way the findings can be interpreted more detailed and comparatively. Data obtained from website of Worldbank (3 basic international tourism statistics of 159 countries for the year 2010) and findings are gained using NCSS (statistical software) 2007. According to the findings of fuzzy clustering method, Turkey belogs to a cluster which contains ABD, United Kingdom, China, Austria, France, Germany, Italy, Malaysia, Spain, Hong Kong, Russian Federation, and Ukraine. According to the findings of classical clustering method (k means), Turkey is in the same cluster with same countries except Hong Kong. Also the findings of two techniques are similar about Turkey. Such a result can be expected correspondingly grading the countries about international their tourism data in 2011. Different clustering methods findings are steady about Euroasian countries too. Except Russian Federation and Ukraine all of the other Euroasian countries are located together in same cluster depending upon two different clustering methods. In conclusion two different clustering methods provide consistent (similar) results about the classification of countries according their internatianol tourism statistics.

Classification of Remote Sensing Imagery Based on Density and Fuzzy c-Means Algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Trinh Le Hung ◽  
Mai Dinh Sinh
Keyword(s):  
Classification Accuracy ◽  
Data Clustering ◽  
Minimum Distance ◽  
Satellite Image ◽  
Clustering Methods ◽  
Cluster Centroid ◽  
Fuzzy C Means ◽  
Advantages And Disadvantages ◽  
Fuzzy C Means Algorithm

The goal of data clustering is to divide a set of data into different clusters, so that the data in the same cluster show some similar characteristics. There are many clustering methods for satellite image segmentation, such as k-means, c-means, iso-data, minimum distance algorithms. Each method has certain advantages and disadvantages, but generally they are based on brightness value to divide the pixels of the image in to clusters. Actually, the probability of occurrence of frequency of appearance of pixel has certain effects on clustering results. In this article, the authors propose a method for clustering satellite imagery based on density. It consists of two main steps: find cluster centroid using density and data clustering using fuzzy c-Means algorithm (DFCM). The results obtained in this study can be used to potentially improve classification accuracy of satellite image.

Automatic classification of gait patterns in children with cerebral palsy using fuzzy clustering method

2020 ◽  
Vol 73 ◽  
pp. 189-194
Author(s):  
Hamed Darbandi ◽  
Mina Baniasad ◽  
Soroush Baghdadi ◽  
Aminreza Khandan ◽  
Amirreza Vafaee ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Fuzzy Clustering ◽  
Automatic Classification ◽  
Clustering Method ◽  
Gait Patterns ◽  
Fuzzy Clustering Method ◽  
Children With Cerebral Palsy
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