key note address delivered on 23rd March 2011 in the Workshop on Data Mining and Computational Biology in Bioinformatics, sponsored by DBT India and organised by Unit of Simulation and Informatics, IARI, New Delhi. I do not claim any originality either to slides or their content and in fact aknowledge various web sources.

1. Knowledge Discovery through Data Mining C. Devakumar Indian Council of Agricultural Research New Delhi-110 012 [email_address]

8. + = Data Interestingness criteria Hidden patterns

9. + = Data Interestingness criteria Hidden patterns Type of Patterns

10. + = Data Interestingness criteria Hidden patterns Type of data Type of Interestingness criteria

13. Statistics: Conceptual Model (Hypothesis) Statistical Reasoning “ Proof” (Validation of Hypothesis)

14. Data mining: Mining Algorithm Based on Interestingness Data Pattern (model, rule, hypothesis) discovery

15. Explores Your Data Finds Patterns Performs Predictions

16. Presentation Exploration Discovery Passive Interactive Proactive Role of Software Business Insight Predictive Analysis Canned reporting Ad-hoc reporting OLAP Data mining

20. Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Decision Making Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems

21. Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization

22. Architecture: Typical Data Mining System data cleaning, integration, and selection Database or Data Warehouse Server Data Mining Engine Pattern Evaluation Graphical User Interface Knowledge-Base Database Data Warehouse World-Wide Web Other Info Repositories

30. Tokenization , which splits a text document into a stream of words by removing all punctuation marks and by replacing tabs and other non-text characters with single white spaces Filtering methods remove words like articles, conjunctions, prepositions, etc. Lemmatization methods try to map verb forms to the infinite tense and nouns to their singular form. Stemming methods attempt to build the basic forms of words, for example, by stripping the plural 's' from nouns, the 'ing' from verbs, or other affixes. Additional linguistic preprocessing N-grams individualization, which is n-word generic sequences that do not necessarily correspond to an idiomatic use; Anaphora resolution, which can identify relationships among a linguistic expression (anaphora) and its preceding phrase, thus, determining the corresponding reference; Part-of-speech tagging (POS) determines the part of speech tag, noun, verb, adjective, etc. for each term; Text chunking aims at grouping adjacent words in a sentence; Word Sense Disambiguation (WSD) tries to resolve the ambiguity in the meaning of single words or phrases; Parsing produces a full parse tree of a sentence (subject, object, etc.).

31. Castellano, M. et al. A bioinformatics knowledge discovery in text application for grid Computing BMC Bioinformatics 2009, 10(Suppl 6):S23

32. BIOINFORMATICS ARCHITECTURE The Layer Architecture consisting of GATE 4.0 Toolkit for Text Mining, a Middleware solution written by Java API, the grid infrastructure middleware, and a physical layer that consists of a Gnu/Linux Operating System. The integrated development environment, GATE was used for the text mining process. GATE operated on a collection of scientific publications in full text available on MedLine/Pubmed (in pdf format) using the process of Text Mining

33. Technology Platform