18231979 Data Mining
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The document provides an overview of data mining concepts and techniques. It discusses what data mining is, the data mining process, different types of data mining techniques including characterization, association, classification, clustering and outlier analysis. It also covers major issues in data mining such as methodology, performance, handling different data types, and applications.
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www.cs.uiuc.edu/~hanj
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introduction to data mining tutorial
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Department of Computer Science
University of Illinois at Urbana-Champaign
www.cs.uiuc.edu/~hanj
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This document provides an overview of data mining, including common uses, tools, and challenges related to system performance, security, privacy, and ethics. It discusses how data mining involves extracting patterns from data using techniques like classification, clustering, and association rule learning. Maintaining privacy and anonymity while aggregating data from multiple sources for analysis poses ethical issues. The document also offers tips for gaining access to data and navigating performance concerns when conducting data mining projects.
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This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
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Introduction to DataMining
1) Data mining involves extracting hidden patterns from large datasets to discover useful information. It is an interdisciplinary field drawing from statistics, machine learning, database technology and more.
2) The overall goal is to extract information and transform it into an understandable structure. This includes data cleaning, integration, selection, transformation, mining patterns, and evaluating/presenting the results.
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This document outlines a course on knowledge acquisition in decision making, including the course objectives of introducing data mining techniques and enhancing skills in applying tools like SAS Enterprise Miner and WEKA to solve problems. The course content is described, covering topics like the knowledge discovery process, predictive and descriptive modeling, and a project presentation. Evaluation includes assignments, case studies, and a final exam.
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Data mining final year project in jalandhar
This document provides an introduction to data mining. It defines data mining as the process of extracting interesting and useful patterns from large amounts of data. The document outlines some common applications of data mining such as market analysis, risk analysis, and fraud detection. It also describes the typical steps involved in a data mining process including data cleaning, pattern evaluation, and knowledge presentation. Finally, the document discusses different data mining functionalities like classification, association rule mining, and clustering.
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
Data mining and knowledge discovery
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom in a hierarchy. It then discusses why data mining is needed due to the explosive growth of data from various sources. Data mining is defined as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. The knowledge discovery process involves identifying a problem, mining data to transform it into actionable information, acting on the information, and measuring the results. The document outlines different types of data that can be mined, including structured, transactional, time-series, spatial, multimedia, and web data. Common data mining tasks are also described such as classification, prediction, clustering,
Data mining and knowledge discovery
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the hierarchy from data to wisdom. It then discusses the growth of data from terabytes to petabytes and major sources of data. Key points made include that while data is growing exponentially, most data is not analyzed due to skills shortage. The document defines data mining as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. It outlines the knowledge discovery process and types of knowledge discovery. Finally, it provides examples of data mining applications.
Data mining and knowledge discovery
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom in a hierarchy. It then explains that the growth of data from various sources has created a need for data mining to extract useful knowledge from large data repositories. The key aspects of data mining discussed are that it aims to discover previously unknown, implicit and potentially useful patterns from large data sets in an automated manner. The document outlines the interdisciplinary nature of data mining and its relationship to knowledge discovery in databases. It describes the types of data that can be mined, including structured, transactional, time-series and web data, as well as common data mining tasks like classification, prediction and clustering.
Data mining and knowledge discovery
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the hierarchy from data to wisdom. It then discusses the growth of data from terabytes to petabytes and major sources of data. Key points made include that while data is growing exponentially, most data is not analyzed due to skills shortage. The document defines data mining as the non-trivial extraction of implicit and potentially useful knowledge from large data sets. It outlines the knowledge discovery process and types of knowledge discovery. Finally, it provides examples of data mining applications.
Data mining and knowledge discovery
The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom. It then explains that the growth of data from various sources has created a need for data mining to extract useful knowledge from large data repositories. Data mining involves non-trivial analysis of implicit patterns in large data sets. It is an interdisciplinary field that draws from areas like machine learning, statistics, database technology, and visualization. The goal is to transform data into actionable information through an iterative process of identifying problems, mining data, acting on results, and measuring impact.
Data mining and knowledge discovery
The document discusses data mining and knowledge discovery from large datasets. It begins by defining the terms data, information, knowledge, and wisdom. It then explains that the growth of data from various sources has created a need for data mining to extract useful knowledge from large datasets. Data mining involves automated analysis techniques from fields like machine learning, statistics, and database management to discover patterns and relationships in data. The knowledge discovery process involves data preparation, data mining, and evaluation of the extracted patterns. The document provides examples of data mining applications in business, science, fraud detection, and web mining.
Data mining and knowledge discovery
The document discusses data mining and knowledge discovery from large datasets. It begins by defining the hierarchy from data to wisdom. It then discusses the growth of big data from various sources and the need for data mining to extract useful knowledge. Data mining involves applying machine learning, statistics, visualization and database techniques to discover patterns in large datasets. The knowledge discovery process involves data cleaning, transformation, data mining and evaluating/interpreting patterns. The document provides examples of data mining applications in business, fraud detection, text mining and web mining.
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6months industrial training in data mining,ludhiana
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Data Mining and Knowledge Discovery in Business Databases
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18231979 Data Mining
- 18. Data Mining and Business Intelligence August 15, 2009 Data Mining: Concepts and Techniques Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP
- 19. Architecture of a Typical Data Mining System August 15, 2009 Data Mining: Concepts and Techniques Data Warehouse Data cleaning & data integration Filtering Databases Database or data warehouse server Data mining engine Pattern evaluation Graphical user interface Knowledge-base
- 26. Data Mining: Confluence of Multiple Disciplines August 15, 2009 Data Mining: Concepts and Techniques Data Mining Database Technology Statistics Other Disciplines Information Science Machine Learning Visualization
- 30. An OLAM Architecture August 15, 2009 Data Mining: Concepts and Techniques Data Warehouse Meta Data MDDB OLAM Engine OLAP Engine User GUI API Data Cube API Database API Data cleaning Data integration Layer3 OLAP/OLAM Layer2 MDDB Layer1 Data Repository Layer4 User Interface Filtering&Integration Filtering Databases Mining query Mining result