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| Content Provider | ACM Digital Library |
|---|---|
| Author | Rao, C. V. Guru Srinivas, Chintakindi |
| Abstract | Software component clustering is an unsupervised learning approach which is used to cluster the software components. These clusters may then be used to study, analyze, understand behavior of the software components. In this paper, we use the k-means clustering algorithm to cluster the software components. The main difference lies in the use of distance measure which is designed to find the similarity between the software components. We use the distance measure [12], to find the pair wise project distance matrix and apply the k-means algorithm on this distance matrix. The main idea is to use more than one distance measure, to explore consensus based technique, so as to cluster software components, instead of using only one measure to cluster the components. This approach may also be applied for software architecture recovery problem by using our distance measure. |
| Starting Page | 1 |
| Ending Page | 5 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450334181 |
| DOI | 10.1145/2832987.2833080 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-09-24 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Components Feature vector Similarity Software Clustering |
| Content Type | Text |
| Resource Type | Article |
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