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Sentence Level Clustering using Fuzzy Relational Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 120 - Number 21 |
| Year of Publication: 2015 |
| Authors: Snehal Raundal, C. R. Barde |
10.5120/21348-4157
|
Snehal Raundal, C. R. Barde . Sentence Level Clustering using Fuzzy Relational Algorithm. International Journal of Computer Applications. 120, 21 ( June 2015), 1-5. DOI=10.5120/21348-4157
Abstract
Clustering is an extremely studied in data mining problem for the text mining domains. Difficulty finds in various applications like customer segmentation, visualization, and collaborative filtering, classification, indexing and document organization. In text mining, clustering the sentence is the processes and it is used within a general text mining tasks. Some of the clustering algorithms and methods are used for clustering the documents at sentence level. In text clustering, sentence level clustering plays a important role this is used in text mining activities. The cluster size may change from one cluster to another and traditional clustering algorithms have some problems in clustering while the input in the form of data set. This gives problems such as, instability of clusters, sensitivity and complexity. To overcome the limitations of those clustering algorithms, in this paper proposes a algorithm called Fuzzy Relational Eigenvector Centrality-based Clustering Algorithm (FRECCA) which is used for the clustering of sentences. We can obtain the more efficient method to overcome the problems in these existing approaches.
References
- Andrew Skabar and Khaled Abdalgader. Clustering sentencelevel text using a novel fuzzy relational clustering algorithm. IEEE Transl. on Knowledge and data Engineering, 25(1):62– 75, January 2013.
- H. Zha. Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering. pages 113–120, 2002.
- R. M. Aliguyev. A new sentence similarity measure and sentence based extractive technique for automatic text summarization. Expert Systems with Applications, 36:7764– 7772, 2009.
- G. Salton. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, 1989.
- C. D. Manning, P. Raghavan, and H. Schutze. Introduction to Information Retrieval. Cambridge Univ. Press, 2008.
- P. Corsini, F. Lazzerini, and F. Marcelloni. A new fuzzy relational clustering algorithm based on the fuzzy c-means algorithm. Soft Computing, 9:439–447, 2005.
- D. Wang, T. Li, S. Zhu, and C. Ding. Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization. Multi-Document Summarization via Sentence-Level Semantic Analysis and Symmetric Matrix Factorization, pages 307–314, 2008.
- K. Sarkar. Sentence clustering-based summarization of multiple text documents. TECHNIAInternational Journal of Computing Science and Communication Technologies, 2(1):325 – 335, July 2009.
- Seema V. Wazarkar and Amrita A. Manjrekar. Text clustering using hfrecca and rough k-means clustering algorithm. Discovery, 15(40):44– 47, April 2014.
- A. Pimpalkar, T. Wandhe, M. Swati Rao, and M. Kene. Review of online product using rule based and fuzzy logic with smileys. International Journal of Computing and Technology, 1(1):39 – 44, February 2014.
- Yuhan Li. , D. McLean, Z. A. Bandar, J. D. OShea, and K. Crockett. Sentence similarity based on semantic nets and corpus statistics. IEEE Transactions on Knowledge and Data Engineering, 18:1138 – 1150, June 2006.
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