A Comparative Study between of Fuzzy C-Means Algorithms and Density based Spatial Clustering of Applications with Noise
Data mining technology has emerged as a means of identifying patterns and trends from large amounts of data and is a computing intelligence area that provides tools for data analysis, new knowledge discovery, and autonomous decision making. Data clustering is an important problem in many areas. Fuzz...
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| Vydáno v: | International journal of engineering & technology (Dubai) Ročník 7; číslo 3.33; s. 131 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
2018
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| ISSN: | 2227-524X, 2227-524X |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Data mining technology has emerged as a means of identifying patterns and trends from large amounts of data and is a computing intelligence area that provides tools for data analysis, new knowledge discovery, and autonomous decision making. Data clustering is an important problem in many areas. Fuzzy C-Means(FCM)[11,12,13] is a very important clustering technique based on fuzzy logic. DBSCAN(Density Based Spatial Clustering of Applications with Noise)[8] is a density-based clustering algorithm that is suitable for dealing with spatial data including noise and is a collection of arbitrary shapes and sizes. In this paper, we compare and analyze the performance of Fuzzy C-Means and DBSCAN algorithms in different data sets. |
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| ISSN: | 2227-524X 2227-524X |
| DOI: | 10.14419/ijet.v7i3.33.18592 |