Refine your search
Collections
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Suruliandi, A.
- Supervised Alias Name Validation Using Statistical Similarity Coefficients
Abstract Views :263 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
Source
ICTACT Journal on Soft Computing, Vol 6, No 1 (2015), Pagination: 1070-1075Abstract
Alias name is the surnames for a known name. Extracting and validating alias names is an interesting research topic in language processing and has a number of Natural language processing applications like Information extraction, Information retrieval, Sentimental analysis, Question and answering. Alias name validation involves the process of validating whether a name is alias name or not. In this work, seven statistical similarity coefficients were used as features in classifier to validate alias names. For each name-alias pair, seven statistical similarity coefficient values were calculated and used as features to train a classifier. The trained classifier is then employed to classify whether a name-alias pair is valid or not. Experiments were conducted using Indian name-alias data that has data for 15 persons containing 35 name-alias pairs. Results show that SVM classifier with Radial Basis Function Kernel outperforms all the other classifiers in terms of overall accuracy.Keywords
Alias Name Extraction, Information Extraction, Web Mining.
References
- Paul Hsiung, Andrew Moore, Daniel Neill and Jeff Schneider, “Alias detection in link data sets”, Proceedings of the International Conference on Intelligence Analysis, 2005.
- Wee Meng Soon, Hwee Tou Ng and Daniel Chung Yong Lim, “A machine learning approach to coreference resolution of noun phrases”, Association for Computational Linguistics, Vol. 27, No. 4, pp. 521-544, 2001.
- James Mayfield, David Alexander, Bonnie J. Dorr, Jason Eisner, Tamer Elsayed, Tim Finin, Clayton Fink et al. “Cross-Document Coreference Resolution: A Key Technology for Learning by Reading”, AAAI Spring Symposium: Learning by Reading and Learning to Read, pp. 65-70, 2009.
- Shalom Lappin and Herbert J. Leass, “An algorithm for pronominal anaphora resolution”, Association for Computational linguistics, Vol. 20, No. 4, pp. 535-561, 1994.
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka, “Automatic discovery of personal name aliases from the web”, IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 6, pp. 831-844, 2011.
- Christopher D. Manning and Hinrich Schutze, “Foundations of Statistical Natural Language Processing”, MIT Press, 1999.
- Tomoko Hokama and Hiroyuki Kitagawa, “Extracting mnemonic names of people from the web”, Digital Libraries: Achievements, Challenges and Opportunities: Lecture Notes in Computer Science, Vol. 4312, pp. 121-130, 2006.
- Vinay Bhat, Tim Oates, Vishal Shanbhag and Charles Nicholas, “Finding aliases on the web using latent semantic analysis”, Data & Knowledge Engineering, Vol. 49, No. 2, pp. 129-143, 2004.
- Hsin-Hsi Chen, Ming-Shun Lin and Yu-Chuan Wei, “Novel association measures using web search with double checking”, Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pp. 1009-1016, 2006.
- William W. Cohen, Pradeep Ravikumar and Stephen E. Fienberg, “A comparison of string metrics for matching names and records”, KDD Workshop On Data Cleaning And Object Consolidation, Vol. 3, pp. 73-78, 2003.
- Tarique Anwar, Muhammad Abulaish and Khaled Alghathbar, “Web content mining for alias identification: A first step towards suspect tracking”, Proceedings of the IEEE International Conference on Intelligence and Security Informatics, pp. 195-197, 2011.
- Peter Mika, “Bootstrapping the foaf-web: An experiment in social networking network mining”, Proceedings of 1st Workshop on Friend of a Friend, Social Networking and the Semantic Web, 2004.
- Yutaka Matsuo, Junichiro Mori, Masahiro Hamasaki, Takuichi Nishimura, Hideaki Takeda, Koiti Hasida and Mitsuru Ishizuka, “POLYPHONET: an advanced social network extraction system from the web”, Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 5, No. 4, pp. 262-278, 2006.
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka, “A web search engine-based approach to measure semantic similarity between words”, IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 7, pp. 977-990, 2011.
- Danushka Bollegala, Yutaka Matsuo, Taiki Honma and Mitsuru Ishizuka, “Identification of personal name aliases on the web”, Proceedings of 17th International Conference on World Wide Web, 2008.
- Kenneth Ward Church and Patrick Hanks, “Word association norms, mutual information, and lexicography”, Computational linguistics, Vol. 16, No. 1, pp. 22-29, 1990.
- Rudi L Cilibrasi and Paul MB Vitanyi, “The Google similarity distance”, IEEE Transactions on Knowledge and Data Engineering, Vol. 19, No. 3, pp. 370-383, 2007.
- http://in.mathworks.com/help/stats/naive-bayes-classification.html
- Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan et al. “Top 10 algorithms in data mining”, Knowledge and Information Systems, Vol. 14, No. 1, pp. 1-37, 2008.
- Ian H Witten and Eibe Frank, “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, 2005.
- Ning An, Lilli Jiang, Jianyonng Wang, Ping Luoe, Min Wange and Bing Nan Li, “Towards detecting of alias without string similarity”, Information Sciences, Vol. 261, pp. 89-100, 2014.
- W. Bruce Croft, Donald Metzler and Trevor Strohman, “Search engines: Information retrieval in practice”, Addison-Wesley, 2010.
- Validating the Performance of Personalization Techniques in Search Engine
Abstract Views :171 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
Source
ICTACT Journal on Soft Computing, Vol 5, No 3 (2015), Pagination: 965-970Abstract
User profiling is an important and basic component in personalized search engine. Search engines respond to a user's query by using the bag-of-words model, which matches keyword between the query and web documents but ignore contexts and users' preferences. Personalized search greatly improves the search results as of the results provided by the search engine without personalization. In this paper, the performance of personalized search based on content analysis and personalized search based on user group have been evaluated. In personalized search based on content analysis the contents are traced by finding the user's browsed documents and search history, which reduce the users search time. In user profile only user preference alone is taken into consideration. The experimental results show that the personalized search based on user group method having higher precision and recall rate than the content analysis method.Keywords
Search Engine, Personalization, User Profile, Content Analysis.- Performance Evaluation of Distance Measures in Proposed Fuzzy Texture Model for Land Cover Classification of Remotely Sensed Image
Abstract Views :142 |
PDF Views:0
Authors
S. Jenicka
1,
A. Suruliandi
1
Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
Source
ICTACT Journal on Soft Computing, Vol 4, No 3 (2014), Pagination: 727-737Abstract
Land cover classification is a vital application area in satellite image processing domain. Texture is a useful feature in land cover classification. The classification accuracy obtained always depends on the effectiveness of the texture model, distance measure and classification algorithm used. In this work, texture features are extracted using the proposed multivariate descriptor, MFTM/MVAR that uses Multivariate Fuzzy Texture Model (MFTM) supplemented with Multivariate Variance (MVAR). The K_Nearest Neighbour (KNN) algorithm is used for classification due to its simplicity coupled with efficiency. The distance measures such as log likelihood, Manhattan, Chi squared, Kullback Leibler and Bhattacharyya were used and the experiments were conducted on IRS P6 LISS-IV data. The classified images were evaluated based on error matrix, classification accuracy and Kappa statistics. From the experiments, it is found that log likelihood distance with MFTM/MVAR descriptor and KNN classifier gives 95.29% classification accuracy.Keywords
Land Cover Classification, Kullback Leibler, Log Likelihood, Chi Squared, Bhattacharyya.- Survey on Crime Analysis and Prediction Using Data Mining Techniques
Abstract Views :603 |
PDF Views:9
Authors
Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 3 (2017), Pagination: 1459-1466Abstract
Data Mining is the procedure which includes evaluating and examining large pre-existing databases in order to generate new information which may be essential to the organization. The extraction of new information is predicted using the existing datasets. Many approaches for analysis and prediction in data mining had been performed. But, many few efforts has made in the criminology field. Many few have taken efforts for comparing the information all these approaches produce. The police stations and other similar criminal justice agencies hold many large databases of information which can be used to predict or analyze the criminal movements and criminal activity involvement in the society. The criminals can also be predicted based on the crime data. The main aim of this work is to perform a survey on the supervised learning and unsupervised learning techniques that has been applied towards criminal identification. This paper presents the survey on the Crime analysis and crime prediction using several Data Mining techniques.Keywords
Criminology, Crime Analysis, Crime Prediction, Data Mining.References
- Mugdha Sharma, “Z-Crime: A Data Mining Tool for the Detection of Suspicious Criminal Activities based on the Decision Tree”, International Conference on Data Mining and Intelligent Computing, pp. 1-6, 2014.
- Kaumalee Bogahawatte and Shalinda Adikari, “Intelligent Criminal Identification System”, Proceedings of 8th IEEE International Conference on Computer Science and Education, pp. 633-638, 2013.
- Jyoti Agarwal, Renuka Nagpal and Rajni Sehgal, “Crime Analysis using K-Means Clustering”, International Journal of Computer Applications, Vol. 83, No. 4, pp. 1-4, 2013.
- Rasoul Kiani, Siamak Mahdavi and Amin Keshavarzi, “Analysis and Prediction of Crimes by Clustering and Classification”, International Journal of Advanced Research in Artificial Intelligence, Vol. 4, No. 8, pp. 11-17, 2015.
- Shiju Sathyadevan, M.S. Devan and S. Surya Gangadharan, “Crime Analysis and Prediction using Data Mining”, Proceedings of IEEE 1st International Conference on Networks and Soft Computing, pp. 406-412, 2014.
- Yu-Yueh Huang, Cheng-Te Li and Shyh-Kang Jeng, “Mining Location-based Social Networks for Criminal Activity Prediction”, Proceedings of 24th IEEE International Conference on Wireless and Optical Communication, pp. 185-190, 2015.
- Jeroen S. De Bruin, Tim K. Cocx, Walter A. Kosters, Jeroen F. J. Laros and Joost N. Kok, “Data Mining Approaches to Criminal Career Analysis”, Proceedings of 6th IEEE International Conference on Data Mining, pp. 1-7, 2006.
- Ehab Hamdy, Ammar Adl, Aboul Ella Hassanien, Osman Hegazy and Tai-Hoon Kim, “Criminal Act Detection and Identification Model”, Proceedings of 7th International Conference on Advanced Communication and Networking, pp. 79-83, 2015.
- Kamal Taha and Paul D. Yoo, “SIIMCO: A Forensic Investigation Tool for Identifying the Influential Members of a Criminal Organization”, IEEE Transactions on Information Forensics and Security, Vol. 11, No. 4, pp. 811-822, 2016.
- Kevin Sheehy et al., “Evidence-based Analysis of Mentally 111 Individuals in the Criminal Justice System”, Proceedings of IEEE Systems and Information Engineering Design Symposium, pp. 250-254, 2016.
- David J. Hand, Heikki Mannila and Padhraic Smyth, “Principles of Data Mining”, MIT Press, 2001.
- 10 Emerging Technologies That Will Change Your World, Available at: http://www.rle.mit.edu/thz/documents/10_emerging_tech.pdf.
- U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, “The KDD Process for Extracting Useful Knowledge from Volumes of Data”, Communications of the ACM, Vol. 39, No. 11, pp. 27-34, 1996.
- Illhoi Yoo, Patricia Alafaireet, Miroslav Marinov, Keila Pena-Hernandez, Rajitha Gopidi, Jia-Fu Chang and Lei Hua, “Data Mining in Healthcare and Biomedicine: A Survey of the Literature”, Journal of Medical Systems, Vol. 36, No. 4, pp. 2431-2448, 2011.
- Shyam Varan Nath, “Crime Pattern Detection using Data Mining”, Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, pp. 1-4, 2006.
- Hsinchun Chen, Wingyan Chung, Yi Qin, Michael Chau, Jennifer Jie Xu, Gang Wang, Rong Zheng and Homa Atabakhsh, “Crime Data Mining: An Overview and Case Studies”, Proceedings National Conference on Digital Government Research, pp. 1-5, 2003.
- Tong Wang et al., “Learning to Detect Patterns of Crime”, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 515-530, 2013.
- Karl F. Schuessler and Donald R. Cressey, “Personality Characteristics of Criminals”, American Journal of Sociology, Vol. 55, No. 5, pp. 476-484, 1950.
- H. Chen, W. Chung, J.J. Xu, G. Wang, Y. Qin and M. Chau, “Crime Data Mining: a General Framework and Some Examples”, Computer, Vol. 37, No. 4, pp. 50-56, 2004.
- Chung-Hsien Yu, Max W. Ward, Melissa Morabito and Wei Ding, “Crime Forecasting using Data Mining Techniques”, Proceedings of 11th IEEE International Conference on Data Mining Workshops, pp. 779-786, 2011.
- P. Thongtae and S. Srisuk, “An Analysis of Data Mining Applications in Crime Domain”, Proceedings of IEEE 8th International Conference on Computer and Information Technology Workshops, pp. 122-126, 2008.
- Rizwan Iqbal, Masrah Azrifah Azmi Murad, Aida Mustapha, Payam Hassany Shariat Panahy and Nasim Khanahmadliravi, “An Experimental Study of Classification Algorithms for Crime Prediction”, Indian Journal of Science and Technology, Vol. 6, No. 3, pp. 4219-4225, 2013.
- Shyam Varan Nath, “Crime Data Mining”, Proceedings of Advances and Innovations in Systems, Computing Sciences and Software Engineering, pp. 405-409, 2007.
- Donald E.Brown, “The Regional Crime Analysis Program (RECAP): A Framework for Mining Data to Catch Criminals”, Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, pp. 2848-2853, 1998.
- Colleen McCue, “Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis”, Butterworth-Heinemann, 2014.
- Arunima S. Kumar and Raju K. Gopal, “Data Mining based Crime Investigation Systems: Taxonomy and Relevance”, Proceedings of Global Conference on IEEE Communication Technologies, pp. 850-853, 2015.
- Manish Gupta, B. Chandra and M.P. Gupta, “Crime Data Mining for Indian Police Information System”, Journal of Crime, Vol. 2, No. 6, pp. 43-54, 2006.