Name
Abstract | ||
|---|---|---|
Fast and high quality document clustering is an important task in organizing
information, search engine results obtaining from user query, enhancing web
crawling and information retrieval. With the large amount of data available and
with a goal of creating good quality clusters, a variety of algorithms have
been developed having quality-complexity trade-offs. Among these, some
algorithms seek to minimize the computational complexity using certain
criterion functions which are defined for the whole set of clustering solution.
In this paper, we are proposing a novel document clustering algorithm based on
an internal criterion function. Most commonly used partitioning clustering
algorithms (e.g. k-means) have some drawbacks as they suffer from local optimum
solutions and creation of empty clusters as a clustering solution. The proposed
algorithm usually does not suffer from these problems and converge to a global
optimum, its performance enhances with the increase in number of clusters. We
have checked our algorithm against three different datasets for four different
values of k (required number of clusters). |
Year | Venue | Keywords |
|---|---|---|
2010 | Clinical Orthopaedics and Related Research | web crawling,document clustering,information retrieval,k means,computational complexity |
Field | DocType | Volume |
Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Canopy clustering algorithm,Data stream clustering,Correlation clustering,Information retrieval,Determining the number of clusters in a data set,Constrained clustering,Machine learning | Journal | abs/1003.1 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alok Ranjan | 1 | 2 | 1.08 |
| Harish Verma | 2 | 0 | 0.68 |
| Eatesh Kandpal | 3 | 0 | 0.34 |
| Joydip Dhar | 4 | 37 | 12.11 |