Academia.eduAcademia.edu

Figure 1 – uploaded by IJCSMC Journal

See full PDFdownloadDownload figure
Partitioning methods a are classified into two subcategories viz. centroid and medoid algorithms. Centroid algorithms represent each cluster by using the gravity centre of the instances. The Mediod algorithm represents each cluster by means of the occurrences closest to gravity centre. Partitioning clustering algorithms try to locally improve a certain criterion. They compute the values of the similarity or distance, they order the results, and pick the one that optimizes the criterion. Hence, the majority of them could be considered as greedy-like algorithms. [7]

Figure 1 Partitioning methods a are classified into two subcategories viz. centroid and medoid algorithms. Centroid algorithms represent each cluster by using the gravity centre of the instances. The Mediod algorithm represents each cluster by means of the occurrences closest to gravity centre. Partitioning clustering algorithms try to locally improve a certain criterion. They compute the values of the similarity or distance, they order the results, and pick the one that optimizes the criterion. Hence, the majority of them could be considered as greedy-like algorithms. [7]

Related Figures (3)

Features in different clusters are relatively independent; the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree (MST) clustering method.
Features in different clusters are relatively independent; the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree (MST) clustering method.
Table 4,1.; Advantages and Disadvantages 5. COMPARATIVE STUDY OF EXISTING METHODS
Table 4,1.; Advantages and Disadvantages 5. COMPARATIVE STUDY OF EXISTING METHODS
N =number of objects, K = number of clusters, S = size of sample.
N =number of objects, K = number of clusters, S = size of sample.
Related topics:
Clustering and Classification Methods
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved