Algorithms for clustering data
Algorithms for clustering data
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning Feature Selection for Medical Databases
CBMS '99 Proceedings of the 12th IEEE Symposium on Computer-Based Medical Systems
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Prediction of protein protein interactions from primary sequences
International Journal of Data Mining and Bioinformatics
Feature selection for genomic data sets through feature clustering
International Journal of Data Mining and Bioinformatics
Practical issues on privacy-preserving health data mining
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Discovering prediction model for environmental distribution maps
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Detecting community structure in complex networks by optimal rearrangement clustering
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
The HIV data mining tool for government decision-making support
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Cancer identification based on DNA microarray data
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
IEEE Transactions on Information Theory
Analysis of diabetic patients through their examination history
Expert Systems with Applications: An International Journal
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Medical datasets hold huge number of records about the patients, the doctors and the diseases. The extraction of useful information which will provide knowledge in decision making process for the diagnosis and treatment of the diseases are becoming increasingly determinant. Knowledge Discovery and data mining make use of Artificial Intelligence (AI) algorithms which are applied to discover hidden patterns and relations in complex datasets using intelligent agents. The existing data mining algorithms and techniques are designed to solve the individual problems, such as classification or clustering. Up till now, no unifying theory is developed. Among the different algorithms in data mining for prediction, classification, interpretation and visualization, 'k-means clustering', 'Decision Trees (C4.5)', 'Neural Network (NNs)' and 'Data Visualization (2D or 3D scattered graphs)' algorithms are frequently utilized in data mining tools. The choice of the algorithm depends on the intended use of extracted knowledge. In this paper, the mentioned algorithms are unified into a tool, called Unified Medical Data Miner (UMDM) that will enable prediction, classification, interpretation and visualization on a diabetes dataset.