The week's pick
Random Articles
Reseach Article
Clustering the Age Classified Preprocessed Automated Blood Cell Counter Data using K-Means First Distinct Element Selection and Random Selection Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 79 - Number 12 |
| Year of Publication: 2013 |
| Authors: D. Minnie, S. Srinivasan |
10.5120/13792-1776
|
D. Minnie, S. Srinivasan . Clustering the Age Classified Preprocessed Automated Blood Cell Counter Data using K-Means First Distinct Element Selection and Random Selection Algorithms. International Journal of Computer Applications. 79, 12 ( October 2013), 17-23. DOI=10.5120/13792-1776
Abstract
The raw Complete Blood Count (CBC) or Full Blood Count (FBC) data from an Automated Blood Cell Counter are collected and transformed in to a Preprocessed and Flattened data using the preprocessing phases of the Knowledge Discovery in Databases. The data is classified into child and adult data sets. The transformed data is used to create clusters of the database in this paper. The K-Means algorithm with two initial mean selection such as first element selection and random element selection is applied on the attributes of the Automated Blood Cell Counter Data to form various clusters. Twelve thousand records are taken from a clinical laboratory for processing.
References
- Complete Blood Count (CBC) with Five-Part Differential NHANES 2003–2004, pp. 3 – 4.
- Automated Blood Cell Counter: www. medscape. com
- Jaiwei Han, Michelinne Kamber, Data Mining : Concepts and Techniques, Morgan Kaufmann Publishers, Second Edition, 2006
- Margaret H. Dunham, Data Mining: Introductory and Advanced Topics, Pearson Education, 2007.
- Alp Aslandogan Y. and Gauri A. Mahajani, Evidence Combination in Medical Data Mining, Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04), Volume 2, 2004, pp. 465 – 469
- Suguna, N. and K. G. Thanushkodi, 2011. Predicting missing attribute values using k-means clustering. Journal of Computer Science, Volume 7, issue 2, pp. 216-224.
- Massoud Toussi, Jean-Baptiste Lamy, Philippe Le Toumelin, and Alain Venot, Using data mining techniques to explore physicians' therapeutic decisions when clinical guidelines do not provide recommendations: methods and example for type 2 diabetes, BMC Medical Informatics and Decision Making 2009; pp. 9:28
- Patricia Cerrito, John C. Cerrito, Data and Text Mining the Electronic Medical Record to Improve Care and to Lower Costs, Proceedings of SUGI 31, March 26 – 29, 2006 paper 077-31, 2006
- Cios KJ, Moore GW, Uniqueness of Medical Data Mining, Artificial Intelligence in Medicine, 2002 Sep-Oct; 26(1-2): 2002, pp. 1- 24.
- Minnie D, Srinivasan S, Application of Knowledge Discovery in Database to Blood Cell Counter Data to Improve Quality Control in Clinical Pathology, Proceedings of 6th International Conference on Bio Inspired Computing – Theory and Applications 2011, September 2011, pp 338 – 342.
- Minnie D, Srinivasan S, Preprocessing and Genearation of Association Rules for Automated Blood Cell Counter Data in Haematology, Proceedings of International Conference on Recent Advances in Computing and Software Systems 2012, April 2012, pp 27 – 32.
- Minnie D, Srinivasan S, Clustering the Preprocessed Automated Blood Cell Counter Data using modified K-Means algorithms and Generation of Association Rules, International Journal of Computer Applications (IJCA), August 2012, 52(17), Foundation of Computer Science, New York, USA, pp. 38-42.
- Asha Gowda Karegowda, T. Vidya, Shama, M. A. Jayaram, A. S. Manjunath, Improving Performance of K-Means Clustering by Initializing Cluster Centers Using Genetic Algorithm and Entropy Based Fuzzy Clustering for Categorization of Diabetic Patients, Proceedings of International Conference on Advances in Computing, Advances in Intelligent Systems and Computing Volume 174, 2012, pp 899-904.
Index Terms
Keywords