Abstract
Data mining is the process of analyzing data and discovering useful information. Sometimes it is called knowledge Discovery. Clustering refers to groups whereas data are grouped in such a way that the data in one cluster are similar, data in different clusters are dissimilar. Many data mining technologies are developed for customer segmentation. PCA is working as a preprocessor of Fuzzy C means and K- means for reducing the high dimensional and noisy data. There are many clustering method apply on customer segmentation. In this paper the performance of Fuzzy C means and K-means after implementing Principal Component Analysis is analyzed. We analyze the performance on a standard dataset for these algorithms. The results indicate that PCA based fuzzy clustering produces better results than PCA based K-means, and is a more stable method for customer segmentation.
| Original language | English |
|---|---|
| Pages (from-to) | 70-74 |
| Number of pages | 5 |
| Journal | International Journal of Scientific and Technology Research |
| Volume | 4 |
| Issue number | 8 |
| Publication status | Published - Oct 2015 |
| Externally published | Yes |
Bibliographical note
Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- Data Mining
- Clustering
- K-means
- Principal component analysis
- Fuzzy C means
- Customer segmentation
- Crisp Set