@article{Luo20091271,
  abstract = {Clustering is a very powerful data mining technique for topic discovery from text documents. The partitional clustering algorithms, such as the family of k-means, are reported performing well on document clustering. They treat the clustering problem as an optimization process of grouping documents into k clusters so that a particular criterion function is minimized or maximized. Usually, the cosine function is used to measure the similarity between two documents in the criterion function, but it may not work well when the clusters are not well separated. To solve this problem, we applied the concepts of neighbors and link, introduced in [S. Guha, R. Rastogi, K. Shim, ROCK: a robust clustering algorithm for categorical attributes, Information Systems 25 (5) (2000) 345-366], to document clustering. If two documents are similar enough, they are considered as neighbors of each other. And the link between two documents represents the number of their common neighbors. Instead of just considering the pairwise similarity, the neighbors and link involve the global information into the measurement of the closeness of two documents. In this paper, we propose to use the neighbors and link for the family of k-means algorithms in three aspects: a new method to select initial cluster centroids based on the ranks of candidate documents; a new similarity measure which uses a combination of the cosine and link functions; and a new heuristic function for selecting a cluster to split based on the neighbors of the cluster centroids. Our experimental results on real-life data sets demonstrated that our proposed methods can significantly improve the performance of document clustering in terms of accuracy without increasing the execution time much.},
  author = {Luo, Congnan and Li, Yanjun and Chung, Soon M.},
  doi = {10.1016/j.datak.2009.06.007},
  interhash = {bf59c4cf26cbc35d6142630b34a66d37},
  intrahash = {13483e90d8b46ef9435ec71473aacee4},
  issn = {0169-023X},
  journal = {Data & Knowledge Engineering},
  note = {Including Special Section: Conference on Privacy in Statistical Databases (PSD 2008) - Six selected and extended papers on Database Privacy},
  number = 11,
  pages = {1271 - 1288},
  title = {Text document clustering based on neighbors},
  url = {http://www.sciencedirect.com/science/article/B6TYX-4WNB4Y8-1/2/1dcd00d9c049988da53b44a526dd6555},
  volume = 68,
  year = 2009
}

@inproceedings{conf/sigir/HuFCZLYC08,
  author = {Hu, Jian and Fang, Lujun and Cao, Yang and Zeng, Hua-Jun and Li, Hua and Yang, Qiang and Chen, Zheng},
  booktitle = {SIGIR},
  crossref = {conf/sigir/2008},
  date = {2008-07-27},
  editor = {Myaeng, Sung-Hyon and Oard, Douglas W. and Sebastiani, Fabrizio and Chua, Tat-Seng and Leong, Mun-Kew},
  ee = {http://doi.acm.org/10.1145/1390334.1390367},
  interhash = {0a2878165034dcdfacb9045608ec482a},
  intrahash = {76f863a12c0b983ec67682deaec1ada4},
  isbn = {978-1-60558-164-4},
  pages = {179-186},
  publisher = {ACM},
  title = {Enhancing text clustering by leveraging Wikipedia semantics.},
  url = {http://dblp.uni-trier.de/db/conf/sigir/sigir2008.html#HuFCZLYC08},
  year = 2008
}

@article{Lew04,
  author = {Lewis, D. D. and Yang, Y. and Rose, T. G. and Li, F.},
  interhash = {ff940c50e028cb53fc10f99ddd39fe3e},
  intrahash = {0db455903d09c97f4f6ccbfb95c66f9e},
  journal = {Journal of Machine Learning Research},
  number = {Apr},
  pages = {361--397},
  title = {RCV1: A New Benchmark Collection for Text Categorization Research},
  url = {http://www.jmlr.org/papers/volume5/lewis04a/lewis04a.pdf},
  volume = 5,
  year = 2004
}