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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jin-Tsong Jeng Chen-Chia Chuang Sheng-Chieh Chang |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Electr. Eng., Nat. Taiwan Univ., Ilan, Taiwan (Chen-Chia Chuang) || Dept. of Comput. Sci. & Inf. Eng., Nat. Formosa Univ., Yunlin, Taiwan (Jin-Tsong Jeng; Sheng-Chieh Chang) |
| Abstract | Clustering algorithms have been widely used in many different applications such as pattern recognition, data mining. It is unsupervised learning algorithm. At the same, the data sets of similarity partition belong to the same group; otherwise data sets divide other groups in the clustering algorithms. The interval fuzzy c-means (IFCM) clustering method was proposed to deal with symbolic interval data. However, it still has noisy and outliers problems. Hence, in this paper we propose interval fuzzy possibilistic c-means (IFPCM) clustering algorithm to overcome the IFCM clustering algorithm for the symbolic interval data clustering in noisy and outlier environments under smart phone. From the results of simulation shows that the proposed IFPCM clustering algorithm is implemented on windows mobile (smart) phone and demonstrated nice performance as expected. |
| Starting Page | 78 |
| Ending Page | 82 |
| File Size | 404014 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9784907764463 |
| DOI | 10.1109/SICE.2014.6935183 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-09-09 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | SICE |
| Subject Keyword | Clustering algorithms Mobile communication Smart phones Operating systems Noise measurement Noise Linear programming windows mobile phone fuzzy clustering interval fuzzy possibilistic c-means clustering algorithm symbolic interval data |
| Content Type | Text |
| Resource Type | Article |
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