Knowledge discovery thru data mining
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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.
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Lect 1 introduction
The document discusses the field of data mining. It begins by defining data mining and describing its branches including classification, clustering, and association rule mining. It then discusses the growth of data in various domains that has created opportunities for data mining applications. The document outlines the history and development of data mining from empirical science to computational science to data science. It provides examples of data mining applications in various domains like healthcare, energy, climate science, and agriculture. Finally, it discusses future directions and challenges for the field of data mining.
Chapter 1. Introduction.ppt
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Introduction of Data Science and Data Analytics
Data science involves extracting meaningful insights from raw and structured data using scientific methods, technologies, and algorithms. It is a multidisciplinary field that uses tools to manipulate and analyze large amounts of data to find new and useful information. Data science uses powerful hardware, programming, and efficient algorithms to solve data problems and is the future of artificial intelligence. It involves collecting, preparing, analyzing, visualizing, managing, and preserving large data sets. Examples of data science applications include smart watches and Tesla's use of deep learning for self-driving cars.
data.2.pptx
This document provides an overview of data mining. It defines data mining as the process of discovering valid, novel, useful and understandable patterns in large data sets. The document outlines the main components and process of data mining, including related fields. It discusses major data mining techniques and tasks such as classification, clustering and association rule mining. Examples of uses for data mining in areas such as business, science, health and risk analysis are also provided. Challenges and advantages of data mining are briefly covered.
Talk
The document discusses data mining and knowledge discovery in databases. It defines data mining as extracting patterns from large amounts of data. The key steps in the knowledge discovery process are presented as data selection, preprocessing, data mining, and interpretation. Common data mining techniques include clustering, classification, and association rule mining. Clustering groups similar data objects, classification predicts categorical labels, and association rules find relationships between variables. Data mining has applications in many domains like market analysis, fraud detection, and bioinformatics.
Unit 1 (Chapter-1) on data mining concepts.ppt
This document provides an introduction to data mining concepts. It discusses why data mining is important due to the massive growth of data. It defines data mining as the automated analysis of large datasets to discover hidden patterns and unknown correlations. The document presents a multi-dimensional view of data mining, including the types of data that can be mined, the patterns that can be discovered, techniques used, and applications. It provides an overview of the key concepts in data mining.
Lect 1 introduction
This document provides an introduction to data mining and data warehousing. It discusses how the volume of data being collected is growing exponentially in many fields due to advances in data collection technologies. It also describes how data mining can be used to extract useful knowledge and patterns from large datasets to help solve important problems. The document outlines some key techniques in data mining including classification, clustering, and association rule mining. It discusses how data mining draws from fields like machine learning, statistics, and databases to analyze large and complex datasets.
Week-1-Introduction to Data Mining.pptx
1. Data mining involves extracting useful patterns and knowledge from large amounts of data. It can help uncover hidden patterns and relationships to help organizations make better decisions.
2. The document discusses various data mining techniques like classification, clustering, association rule mining and describes how each technique can be applied.
3. It also covers important aspects of data mining like the steps in the knowledge discovery process, different types of databases, visualization techniques, and major issues in data mining.
Data Mining: Concepts and techniques: Chapter 13 trend
Mining Complex Types of Data,
Other Methodologies of Data Mining,
Data Mining Applications,
Data Mining and Society,
Data Mining Trends,
Summary
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
©2013 Han, Kamber & Pei. All rights reserved.
Data Mining Intro
This chapter introduces data mining and discusses its rise due to the massive growth of digital data. It describes data mining as the automated process of discovering patterns and knowledge from large data sets. The chapter outlines several key aspects of data mining, including the types of data that can be mined, the patterns that can be discovered, the technologies used, and its applications across various domains.
01Intro.ppt
This document provides an introduction to data mining concepts and techniques. It discusses why data mining has become important due to the massive growth of digital data. Data mining aims to extract useful patterns from large datasets through techniques like generalization, association analysis, classification, and cluster analysis. It can be applied to many types of data and has uses in domains such as business, science, and healthcare to gain insights and make predictions.
01Introduction to data mining chapter 1.ppt
This chapter introduces data mining and discusses its rise due to the massive growth of digital data. It describes data mining as the automated extraction of meaningful patterns from large data sets, and notes it draws on techniques from machine learning, statistics, pattern recognition, and database systems. The chapter outlines different types of data that can be mined, patterns that can be discovered, and applications of data mining in various domains including business, science, and on the web.
01Intro.ppt
This chapter introduces data mining and discusses its rise due to the massive growth of digital data. It describes data mining as the automated extraction of meaningful patterns from large data sets, and notes it draws on techniques from machine learning, statistics, pattern recognition, and database systems. The chapter outlines different types of data that can be mined, patterns that can be discovered, and applications of data mining in various domains including business, science, and on the web.
data mining
This document provides an introduction to data mining concepts and techniques. It discusses why data mining has become important due to the massive growth of digital data. Data mining aims to extract useful patterns from large datasets through techniques like generalization, association analysis, classification, and cluster analysis. It can be applied to many types of data and has uses in domains such as business, science, and healthcare to help analyze data and discover useful knowledge.
Data Mining Application and Trends
This document discusses data mining applications and trends. It covers topics like mining complex data types, other data mining methodologies, and various applications of data mining. Some key applications discussed include using data mining in finance, retail, telecommunications, science/engineering, intrusion detection, and recommender systems. The document also touches on topics like visual data mining, ubiquitous and invisible data mining, and the privacy and social impacts of data mining.
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Data Mining: Concepts and techniques: Chapter 13 trend
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Knowledge discovery thru data mining
- 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