data mining
TRANSCRIPT
DATA MINING
Presented by- Shweta kumariM.Sc. Bioinformatics1st semesterRoll no-21Central University of Bihar
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
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.
Condition of Data Mining
Data should be extremely large. More the data set, more is the
accuracy of prediction
Properties of data mining
Automatic discovery of pattern Prediction of likely outcomes Creation of actionable information Focus on large data sets and data bases
Objective of Data Mining
To predict future trends To find the hidden trends
/characteristics/patterns
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.
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
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.
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
Thank You