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DATA MINING Presented by- Shweta kumari M.Sc. Bioinformatics 1 st semester Roll no-21 Central University of Bihar

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Page 1: Data mining

DATA MINING

Presented by- Shweta kumariM.Sc. Bioinformatics1st semesterRoll no-21Central University of Bihar

Page 2: Data mining

C0NTENTS

1. Intoduction2. Condition of Data Mining3. Properties of Data Mining4. Objective of Data Mining5. Technique of Data Mining6. Application of Data Mining in

Bioinformatics7. Conclusion & chllenges

Page 3: Data mining

INTRODUCTION

Data mining refers to extracting or mining knowledge from large amount of data.

To dig out the hidden characteristic from all data to predict future trends.

Page 4: Data mining

Condition of Data Mining

Data should be extremely large. More the data set, more is the

accuracy of prediction

Page 5: Data mining

Properties of data mining

Automatic discovery of pattern Prediction of likely outcomes Creation of actionable information Focus on large data sets and data bases

Page 6: Data mining

Objective of Data Mining

To predict future trends To find the hidden trends

/characteristics/patterns

Page 7: Data mining

Technique of Data Mining 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.

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Application of Data Mining in Bioinformatics

Gene finding Protein function domain Function motif detection Protein function inference Disease diagnosis Disease prognosis Disease treatment optimization Protein sub cellular location prediction

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Conclusion & chllengesSince, 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.

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References:- Database system Concept (Abrham Silberschatz,Henry F. Korth,S. Sudarshan)

Wikipedia.org/wiki/Data mining http://www.ijcse.com/docs/IJCSE10-01-02-18.pdf

Page 11: Data mining

Thank You


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