Clustering techniques for web documents
Document clustering is a process of grouping documents into several natural and homogeneous clusters so that documents within the same cluster are more similar to each other than those belonging to other clusters [1]. While in the web environment, task seems more challenging. Essential clustering te...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
2013
|
| 主題: | |
| 在線閱讀: | http://hdl.handle.net/10356/54272 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | Document clustering is a process of grouping documents into several natural and homogeneous clusters so that documents within the same cluster are more similar to each other than those belonging to other clusters [1]. While in the web environment, task seems more challenging. Essential clustering techniques need to be employed to facilitate the discovery knowledge in this process. K-means is one of the frequently used methods in data clustering; however, it will fail to find out the meaningful clustering result if input data is given in a less structured way. Therefore, in this report a new learning distance metric proposed by Eric P. Xing is implemented with supplementary side information to help improving the K-means clustering performance. New algorithm will be studied in details and validated on different datasets and its performance will be evaluated by some quantitative values: NMI, purity and random index using Java as well as cluster visualization using MATLAB. From the results obtained, we have found that new clustering algorithm has shown a pleasant improvement compared with the original one and might be used for future application in data clustering. |
|---|