Data Mining Techniques in the Diagnosis of Tuberculosis

作者: T. Asha , S. Natarajan , K. N. B. Murthy

DOI: 10.5772/30504

关键词: Scientific discoveryKnowledge extractionProfiling (information science)Data miningComputer science

摘要: Data mining is the knowledge discovery process which helps in extracting interesting patterns from large amount of data. With data doubling every three years, becoming an increasingly important tool to transform these into information. It commonly used a wide range profiling practices, such as marketing, surveillance, fraud detection, medical and scientific (J.Han & M.Kamber,2006).

参考文章(36)
Zhonghua Tang, Qin Liao, A New Class Based Associative Classification Algorithm international multiconference of engineers and computer scientists. pp. 685- 689 ,(2007)
Carlos Ordonez, Levien de Braal, Cesar A. Santana, Discovering Interesting Association Rules in Medical Data. international conference on management of data. pp. 78- 85 ,(2000)
Ramakrishnan Srikant, Rakesh Agrawal, Fast Algorithms for Mining Association Rules in Large Databases very large data bases. pp. 487- 499 ,(1994)
Mark A. Hall, Ian H. Witten, Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques ,(1999)
Jiawei Han, Xiaoxin Yin, CPAR: Classification based on Predictive Association Rules. siam international conference on data mining. pp. 331- 335 ,(2003)
Fadi Thabtah, Peter Cowling, Yonghong Peng, None, MCAR: multi-class classification based on association rule acs ieee international conference on computer systems and applications. pp. 33- ,(2005) , 10.1109/AICCSA.2005.1387030
Bavani Arunasalam, Sanjay Chawla, CCCS: a top-down associative classifier for imbalanced class distribution knowledge discovery and data mining. pp. 517- 522 ,(2006) , 10.1145/1150402.1150461
John F. Roddick, Peter Fule, Warwick J. Graco, Exploratory medical knowledge discovery: experiences and issues Sigkdd Explorations. ,vol. 5, pp. 94- 99 ,(2003) , 10.1145/959242.959243
Qiang Niu, Shi-Xiong Xia, Lei Zhang, Association Classification Based on Compactness of Rules 2009 Second International Workshop on Knowledge Discovery and Data Mining. pp. 245- 247 ,(2009) , 10.1109/WKDD.2009.160