Loading...
Please wait, while we are loading the content...
Similar Documents
Implementing K-Means Clustering Algorithm Using MapReduce Paradigm
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rao, B. Chandrasekhara Rambabu, Medara |
| Copyright Year | 2016 |
| Abstract | Clustering is a useful data mining technique which groups’ data points such that the points within a single group have similar characteristics, while the points in different groups are dissimilar. Partitioning algorithm methods such as k-means algorithm is one kind of widely used clustering algorithms. As there is an increasing trend of applications to deal with vast amounts of data, clustering such big data is a challenging problem. Recently, partitioning clustering algorithms on a large cluster of commodity machines using the MapReduce framework have received a lot of attention. Traditional way of clustering text documents is Vector space model, in which tf-idf is used for k-means algorithm with supportive similarity measure. This project exhibits an approach to cluster text documents in which results obtained by executing map reduce k-means algorithm on single node cluster show that the performance of the algorithm increases as the text corpus increases. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.ijsr.net/archive/v5i7/14071601.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
