A comparative analysis of methodologies for database schema integration
ACM Computing Surveys (CSUR)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 02
Analysis of healthcare coverage: A data mining approach
Expert Systems with Applications: An International Journal
Associative classification of mammograms using weighted rules
Expert Systems with Applications: An International Journal
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Medical sciences are rapidly emerging as a data rich discipline where the amount of databases and their dimensionality increases exponentially with time. Data integration algorithms often rely upon discovering embedded, useful, and novel relationships between feature attributes that describe the data. Such algorithms require data integration prior to knowledge discovery, which can lack the timeliness, scalability, robustness, and reliability of discovered knowledge. Knowledge integration algorithms offer pattern discovery on segmented and distributed databases but require sophisticated methods for pattern merging and evaluating integration quality. We propose a unique computational framework for discovering and integrating frequent sets of features from distributed databases and then exploiting them for unsupervised learning from the integrated space. Assorted indices of cluster quality are used to assess the accuracy of knowledge merging. The approach preserves significant cluster quality under various cluster distributions and noise conditions. Exhaustive experimentation is performed to further evaluate the scalability and robustness of the proposed methodology.