Blue print of decision support system for loan based data mining techniques

  • Unique Paper ID: 145516
  • PageNo: 869-872
  • Abstract:
  • A choice emotionally supportive network (DSS) is a PC based application that gathers, composes and breaks down Business information to encourage quality business basic leadership for administration, tasks and arranging. An all around outlined DSS helps leaders in aggregating an assortment of information from numerous sources: crude information, records, individual learning from representatives, administration, administrators and plans of action. DSS investigation encourages organizations to distinguish and take care of issues, and decide. Then again Data Mining (DM) likewise helps in basic leadership by finding the styles and associations in the information accessible. By utilizing the information mining procedures we can remove the learning from information shop, information distribution centers, and particularly cases, even from operational database. In this paper another strategy was proposed to coordinate DSS with DM for advances to the Real Estate improvements subsidize (REDF) clients. Here we are utilizing affiliation procedures for using sound judgment in the information accessible. Apriori is a calculation for visit thing set mining and affiliation administer learning over value-based databases. It continues by distinguishing the successive individual things in the database and extending them to bigger and bigger thing sets as long as those thing sets show up adequately regularly in the database.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{145516,
        author = {P. Gayathri  and Ms. M. Vinayasree},
        title = {Blue print of decision support system for loan based data mining techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {10},
        pages = {869-872},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145516},
        abstract = {A choice emotionally supportive network (DSS) is a PC based application that gathers, composes and breaks down Business information to encourage quality business basic leadership for administration, tasks and arranging. An all around outlined DSS helps leaders in aggregating an assortment of information from numerous sources: crude information, records, individual learning from representatives, administration, administrators and plans of action. DSS investigation encourages organizations to distinguish and take care of issues, and decide. Then again Data Mining (DM) likewise helps in basic leadership by finding the styles and associations in the information accessible. 
By utilizing the information mining procedures we can remove the learning from information shop, information distribution centers, and particularly cases, even from operational database. In this paper another strategy was proposed to coordinate DSS with DM for advances to the Real Estate improvements subsidize (REDF) clients. Here we are utilizing affiliation procedures for using sound judgment in the information accessible.  Apriori  is a calculation for visit thing set mining and affiliation administer learning over value-based databases. It continues by distinguishing the successive individual things in the database and extending them to bigger and bigger thing sets as long as those thing sets show up adequately regularly in the database.},
        keywords = {Decision support system Data mining, Apriori algorithm, Association techniques},
        month = {},
        }

Cite This Article

Gayathri, P., & Vinayasree, M. M. (). Blue print of decision support system for loan based data mining techniques. International Journal of Innovative Research in Technology (IJIRT), 4(10), 869–872.

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