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Data mining
This document provides an introduction and overview of data mining. It discusses how data mining extracts knowledge from large amounts of data to discover hidden patterns and predict future trends. It notes that for effective data mining, data sets need to be extremely large. The document outlines some key techniques of data mining including associative learning, artificial neural networks, clustering, genetic algorithms, and hidden Markov models. It also discusses applications of data mining in bioinformatics such as gene finding, protein function prediction, and disease diagnosis. Finally, it acknowledges that while bioinformatics data is rich, developing comprehensive theories remains challenging but creates opportunities for novel knowledge discovery methods.
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Data mining
- 1. DATA MINING Presented by-Shweta kumari M.Sc. Bioinformatics 1st semester Roll no-21 Central University of Bihar
- 2. C0NTENTS 1. Intoduction 2. Conditionof Data Mining 3. Properties of Data Mining 4. Objective of Data Mining 5. Technique of Data Mining 6. Application of Data Mining in Bioinformatics 7. Conclusion & chllenges
- 3. INTRODUCTION Data miningrefers to extracting or mining knowledge from large amount of data. To dig out the hidden characteristic from all data to predict future trends.
- 4. Condition of DataMining Data should be extremely large. More the data set, more is the accuracy of prediction
- 5. Properties of data mining Automatic discovery of pattern Prediction of likely outcomes Creation of actionable information Focus on large data sets and data bases
- 6. Objective of DataMining To predict future trends To find the hidden trends /characteristics/patterns
- 7. Technique of DataMining ASSOCIATIVE LEARNING – Techniques In which we learn how outcome of one entity is influence by the other. ARTIFICIAL NURAL NETWORK- This is computational model inspired by animal central nervous system which is capable of machine learning as well as pattern recognition. CLUSTERING- It is the task of discovering groups and structure in tha data that are in some way or another similar without using known structure in the data. GENETIC ALGORITHM- It is optimization technique, it mimics the process of evolution viz. inheritance, mutation, selection and crossing over. HIDDEN MARKOV MODEL- It provides a mathematical framework for multiple sequence alignment and finding periodic patterns in a single sequence.
- 8. Application of Data Miningin Bioinformatics Gene finding Protein function domain Function motif detection Protein function inference Disease diagnosis Disease prognosis Disease treatment optimization Protein sub cellular location prediction
- 9. Conclusion & chllenges Since,bioinformatics is data rich, but lacks a comprehensive theory of life’s organization at molecular level. The extensive database of biological information create both challenges and opportunities for development of novel KDD (Knowledge Discovery Database) method.
- 10. References:- Database systemConcept (Abrham Silberschatz,Henry F. Korth,S. Sudarshan) Wikipedia.org/wiki/Data mining http://www.ijcse.com/docs/IJCSE10-01-02-18.pdf
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