Quantitative Bio-Medical Data Analysis and Visualization Using Data Mining and Text Mining Approaches

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V.V.Jaya Rama Krishnaiah
Dr. K.Ramchand H Rao, Prof. K. Mrithyunjaya Rao


In view of today's information avalanche, recent progress in data mining research has led to the development of numerous efficient and scalable methods for mining interesting patterns in large databases. The focus of data analysis and data mining tools in biomedical research highlights the current state of research in the key biomedical research areas such as, medical informatics, public health informatics and biomedical imaging. Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research. Biomedical text data mining is concerned with automated methods for analyzing the content of these documents and discovering and extracting the knowledge in them. Numerical data mining has long been used to uncover patterns in numerical data and make predictions based on those patterns. Text data mining builds on the success of numerical data mining but presents additional challenges. The amount of available biomedical data continues to grow in an exponential rate; however, the impact of utilizing such resources remains minimal. The development of innovative tools to integrate, analyze and mine such data sources is a key step towards achieving larger impact levels. In this Paper, we analyze how data mining may help bio-medical data analysis and outlined some research problems that may motivate the further developments of data mining tools for bio-data analysis and representation of Knowledge.

Keywords: Data Mining, Text Mining, Knowledge Management, Duo Mining, Knowledge Representation, Information Extraction, Information Retrieval


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