Extemporizing the Data Trait
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2018 by IJETT Journal|
|Year of Publication : 2018|
|Authors : Sakshi Jolly , Dr. Neha gupta
|DOI : 10.14445/22315381/IJETT-V58P219|
Sakshi Jolly , Dr. Neha gupta "Extemporizing the Data Trait", International Journal of Engineering Trends and Technology (IJETT), V58(2),100-103 April 2018. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Information quality is a focal issue for some data arranged associations. Late advances in the information quality field mirror the view that a database is the result of an assembling procedure. While routine blunders, for example, non-existent postal divisions can be recognized and amended utilizing conventional information purifying apparatuses, numerous mistakes systemic to the assembling procedure can`t be tended to. Thusly, the result of the information producing process is a loose recording of data about the substances of intrigue (i.e. clients, exchanges or resources). Thusly, the database is just a single (imperfect) adaptation of the elements it should represent. There are numerous arrangement calculations yet choice tree is the most normally utilized calculation due to its simplicity of execution and less demanding to comprehend contrasted with other grouping calculations. In this paper we are actualizing a calculation utilizing weka information mining apparatus utilizing freely accessible datasets of various sizes. This paper additionally gives bits of knowledge into the rate of precision it gives when a dataset contains missing esteems, missing information and vast measure of information
1. Anyanwu, M., and Shiva, S. (2009). Application of Enhanced Decision Tree Algorithm toChurn Analysis. 2009 International Conference on Artificial Intelligence and PatternRecognition (AIPR-09), Orlando Florida
2. Jiawei han and micheline kamber. Data mining concepts and techniques, second edition,285-291
3. Matthew N.anyanwu, Sajjan g. shiva. Comparative analysis of serial decision tree classification algorithms
4. Mehdi piroozma, Youping deng, jack y yang and mary qu yang. A comparative study of different machine learning methods on microarray gene expression data, BMC genomics
5. Tzung-I tang,Gang Zheng, Yalou huang,Guangfu Shu,Pengtao wang. A comparative study of medical data classification methods based on decision tree and system reconstruction analysis. IEMS vol.4,no.1,pp-102-108,june 2005
6. Xu,M, wang, J. Chen, T. (2006). Improved decision tree algorithm: ID3+, intelligent computing in signal Processing and pattern recognition, Vol. 345, PP.141-149.
7. Wayne W. E. (2004) “Data Quality and the Bottom Line: Achieving Business Success through a Commitment to High Quality Data “,The Data warehouse Institute (TDWI) report , available at www.dw-institute.com .
8.The Standish Group (1999), “Migrate Headaches,” available at www.it-cortex.com/start_failure_rate.htm
9. Ralaph Kimball, The Data Warehouse ETL Toolkit, Wiley India (P) Ltd (2004).
10. Tech Notes (2008), Why Data Warehouse Projects Fail: Using Schema Examination Tools to Ensure Information Quality, Schema Compliance, and Project Success. Embarcadero Technologies. Available at www.embarcadero.com.
11. Markus Helfert, Gregor Zellner, Carlos Sousa, “Data Quality Problems and Proactive DataQuality Management in Data- Warehouse-Systems” .
Data quality (DQ), Data Warehouse (DW), ETL(Extraction Transformation Loading)