Manuscript Number : SHISRRJ19224

Data Analysis in SJ Clustering and Mary-Data Query System Analysis using Jasper Server Report 5.5 Tool

Authors(3) :-S. J. MaryJasper, V. SugaPriya, R. KrishnaMoorthy

This paper identifying records that produces compatible results using Fast Clustering Selection Algorithm. A selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a record, the effectiveness is related to the quality of the record. The selection algorithm fetches the result with the help of register number. The Selection algorithm works in two steps. In the first step, the register number fetches the result from the server. The record for every individual will be obtained by hit method. The sender sends the request to the server. In the second step, the most representative record that is strongly related to target classes is fetched from database. The record fetches from the database by the register number. The string generation algorithm is guaranteed to generate the optimal result k candidates. We analyses the results of students using Selection Algorithm. We need to define compatible operation analogs by introducing max-min operation & min-max operation. It automatically collects data from the web to enrich the result. The analysis of result for huge students make more time. The accuracy of the result has to be considered. We need to fetch the result individually by their register number. It leads to time inefficiency. In a proposed system, we obtain the result for a group of students. The Selection method fetches the result for a student according to their register number which is entered in between a range. The result for the student automatically fetched from the server. Once the result for the candidate has been fetched from the server, it stored in the client database. Then we sort the result of the student as group. It increases the accuracy and makes the efficient one. It reduces the burden of the people who analyze the result. The result analysis is performed within a short period. We can generate the report based on the GRADE system. Our experimental evaluation shows that our approach generates superior results. Extensive experiments on large real data sets demonstrate the efficiency and effectiveness. Finally we sort the results of students using JASPER FAST CLUSTERING SELECTION algorithm.

Authors and Affiliations

S. J. MaryJasper
Assistant Professor, Department of Management Studies, Alpha College of Engineering, Chennai, Tamilnadu, India.
V. SugaPriya
Assistant Professor, Department of Management Studies, Alpha College of Engineering, Chennai, Tamilnadu, India.
R. KrishnaMoorthy
P.G Student, Department of Management Studies, Alpha College of Engineering, Chennai, Tamilnadu, India.

Jasper, Minmax and Maxmin Operation.

  1. Chanda P., Cho Y., Zhang A. and Ramanathan M., Mining of Attribute Interactions Using Information Theoretic Metrics, In Proceedings of IEEE international Conference on Data Mining Workshops, pp 350-355, 2009.
  2. Y. Du, S. Gupta, and G. Varsamopoulos, “Improving On-Demand Data Access Efficiency in MANETs with Cooperative Caching,” Ad Hoc Networks, vol. 7, pp. 579-598, May 2009.
  3. Biesiada J. and Duch W., Features election for high-dimensionaldatala Pearson redundancy based filter, AdvancesinSoftComputing, 45, pp 242C249, 2008.
  4. Garcia S and Herrera F., An extension on “Statistical Comparisons of Classifiers over Multiple Data Sets” for all pairwise comparisons, J. Mach. Learn. Res., 9, pp 2677-2694, 2008.
  5. C. Chow, H. Leong, and A. Chan, “GroCoca: Group-Based Peer- to-Peer Cooperative Caching in Mobile Environment,” IEEE J. Selected Areas in Comm., vol. 25, no. 1, pp. 179-191, Jan. 2007.
  6. Demsar J., Statistical comparison of classifiers over multiple data sets, J. Mach. Learn. Res., 7, pp 1-30, 2006.
  7. L. Yin and G. Cao, “Supporting Cooperative Caching in Ad Hoc Networks,” IEEE Trans. Mobile Computing, vol. 5, no. 1, pp. 77-89, Jan. 2006.
  8. Butterworth R., Piatetsky-Shapiro G. and Simovici D.A., On Feature Selectionthrough Clustering, In Proceedings of the Fifth IEEE internationalConference on Data Mining, pp 581-584, 2005.
  9. Fleuret F., Fast binary feature selection with conditional mutual Information, Journal of Machine Learning Research, 5, pp 1531-1555, 2004.
  10. Dhillon I.S., Mallela S. and Kumar R., A divisive information theoretic feature clustering algorithm for text classification, J. Mach. Learn. Res., 3, pp 1265-1287, 2003.
  11. Forman G., An extensive empirical study of feature selection metrics for text classification, Journal of Machine Learning Research, 3, pp 1289-1305, 2003.
  12. Guyon I. and Elisseeff A., An introduction to variable and feature selection, Journal of Machine Learning Research, 3, pp 1157-1182, 2003.
  13. M. Korupolu and M. Dahlin, “Coordinated Placement and Replacement for Large-Scale Distributed Caches,” IEEE Trans. Knowledge and Data Eng., vol. 14, no. 6, pp. 1317-1329, Nov. 2002.
  14. Das S., Filters, wrappers and a boosting-based hybrid for feature Selection, In Proceedings of the Eighteenth International Conference on Machine Learning, pp 74-81, 2001.
  15. Dougherty, E. R., Small sample issues for microarray-based classification. Comparative and Functional Genomics, 2(1), pp 28-34, 2001.
  16. S. Dykes and K. Robbins, “A Viability Analysis of Cooperative Proxy Caching,” Proc. IEEE INFOCOM, 2001.
  17. Bell D.A. and Wang, H., A formalism for relevance and its application in feature subset selection, Machine Learning, 41(2), pp 175-195, 2000.

Publication Details

Published in : Volume 2 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 33-38
Manuscript Number : SHISRRJ19224
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

S. J. MaryJasper, V. SugaPriya, R. KrishnaMoorthy, "Data Analysis in SJ Clustering and Mary-Data Query System Analysis using Jasper Server Report 5.5 Tool", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 2, Issue 2, pp.33-38, March-April.2019
URL : https://shisrrj.com/SHISRRJ19224

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