Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol. 9, Issue 5 , 2022
Data Mining Using Matrix Factorization for Enhancing a Patient’s HealthCare

Author : Manigandan. J 1 Dr. Soundararajan. S 2

Date of Publication :7th March 2016

Abstract: Web mining is used to discover as well as extract data from web documents and service. Social networking sites are used to discuss the current topics and reactions to current happening on the internet. The discussion which reflects the opinion of people, thoughts and their innovative ideas. Detection of current topic and tracking valid data from offline articles is quite difficult. Detection of topic from social networking sites will helps to gather and analyses the huge volume of up-to-the minute. Topics are detected based on vigorously and provides path to various treatments to cure the diseases. The techniques are called as Formal Concept Analysis [3] based on Matrix Factorization are intended to pick up the evolution and issues of current topic in unstructured content which are present in a social media. Extraction and analyses of data based on the user-needed data content. Self organizing maps [16] are used to correlate the data based on positive and negative words present in the user’s status. Scores of text will give as numerical value of each user forums. The pictorial representation can be viewed based on the scored values and for easy understanding. It helps to determine the better treatments and least cost medicine to cure incurable diseases can be identified and try to cure by early stage as soon as possible.

Reference :

    1. Altug Akay, Member, IEEE, Andrei Dragomir, Bjorn Erik Erlandsson, Senior Member, IEEE, “Network-Based Modeling and Intelligent Dapta Mining of Social Media for Improving Care” 2015.
    2. Ping Li, Jiajun Bu, Yi Yang and RongrongJi, “Discriminative Orthogonal Nonnegative Grid Factorization”, Journal on Expert Systems with Applications, 2013 pp. 01–11.
    3. Elena Nenova and Dmitry I. Ignatov, “An FCAbased Boolean Grid Factorization for Collaborative Filtering”, Conference on Formal Concept in information retrieval, 2012 pp. 57–73.
    4. Alan Ritter, Mausam and Oren Etzioni, “Open Domain Event Extraction from Twitter”, Conference on Knowledge discovery and data mining, 2012, pp. 1104–1112.
    5. Anish Das Sarma and Alpa Jain, “Dynamic Relationship and Event Discovery”,Conference on Web search and data mining, 2011, pp. 207–216
    6. Deng Cai and Jiawei Han, “Graph Regularized Nonnegative Grid Factorization for Data Representation”, Transactions on Pattern analysis and Machine Intelligence, 2011, Vol. 33, No. 8, pp. 1548–1560
    7. Abderrahim El Qadi, DrissAboutajdine and YassineEnnouary, „Formal Concept Analysis for Information Retrievalβ€Ÿ, Journal of computer science and information security, 2010, vol.7, No. 2, pp. 119–125
    8. C. Corley, D. Cook, A. Mikler, and K. Singh, “Text and structural data mining of influenza mentions in web and social media,” Int. J. Environ. Res. Public Health, , Feb. 2010, vol. 7, pp. 596–615
    9. Fei Wang, “Community discovery using nonnegative grid factorization”, Conference on data mining and data representation, 2010, pp. 01–29.
    10. Jonas Poelmans, Paul Elzinga and Stijn Viaene, “Formal Concept Analysis in knowledge discovery: a survey”,Conference on conceptual structures, 2010, pp. 139–153.
    11. Vasumathi, D. and Govardhan, ‘Efficient Web usage Mining Based on Formal Concept Analysis’, Journal on Theoretical and Applied Information Technology, , 2009, pp. 99–109
    12. S. R. Das and M. Y. Chen, “Yahoo! for Amazon: Sentiment extraction from small talk on the Web,” Manag. Sci., Sep. 2007, vol. 53, pp. 1375–1388
    13. P. Soucy and G. W. Mineau, “Beyond TFIDF weighting for text catego-rization in the vector space model,” in Proc. 19th Int. Joint Conf. Artificial Intell., Edinburgh, U.K., 2005, pp. 1130–1135.
    14. W. Yih, P. H. Chang, and W. Kim, “Mining online deal forums for hot deals,” in Proc. IEEE/WIC/ACM Int. Conf. Web Intell., Beijing, China, 2004, pp. 384– 390.
    15. B. Taskar, M. Wong, P. Abbeel, and D. Koller, “Link prediction in relational data,” in Proc. Adv. Neural Inform. Process. Syst., Vancouver, B.C. Canada, 2003.
    16. J. Vesanto, J. Himberg, E. Alhoniemi, and J. Parhankangas, “Self-Organizing Map in MATLAB: The SOM Toolbox,” in Proc. Matlab DSP Conf., Espoo, Finland, 1999, pp. 35–40.

Recent Article