Major issues in data mining
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The document discusses major issues in data mining including mining methodology, user interaction, performance, and data types. Specifically, it outlines challenges of mining different types of knowledge, interactive mining at multiple levels of abstraction, incorporating background knowledge, visualization of results, handling noisy data, evaluating pattern interestingness, efficiency and scalability of algorithms, parallel and distributed mining, and handling relational and complex data types from heterogeneous databases.
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1.2 steps and functionalities
Data mining involves multiple steps in the knowledge discovery process including data cleaning, integration, selection, transformation, mining, and pattern evaluation. It has various functionalities including descriptive mining to characterize data, predictive mining for inference, and different mining techniques like classification, association analysis, clustering, and outlier analysis.
Data mining primitives
Data mining primitives include task-relevant data, the kind of knowledge to be mined, background knowledge such as concept hierarchies, interestingness measures, and methods for presenting discovered patterns. A data mining query specifies these primitives to guide the knowledge discovery process. Background knowledge like concept hierarchies allow mining patterns at different levels of abstraction. Interestingness measures estimate pattern simplicity, certainty, utility, and novelty to filter uninteresting results. Discovered patterns can be presented through various visualizations including rules, tables, charts, and decision trees.
Data mining tasks
This document discusses data mining and different types of data mining techniques. It defines data mining as the process of analyzing large amounts of data to discover patterns and relationships. The document describes predictive data mining, which makes predictions based on historical data, and descriptive data mining, which identifies patterns and relationships. It also discusses classification, clustering, time-series analysis, and data summarization as specific data mining techniques.
Data Mining: Application and trends in data mining
This document provides an overview of application trends in data mining. It discusses how data mining is used for financial data analysis, customer analysis in retail and telecommunications, biological data analysis, scientific research, intrusion detection, and more. It also outlines statistical and visualization techniques used in data mining as well as privacy and security considerations. The document concludes by encouraging the reader to explore additional self-help tutorials on data mining tools and techniques.
Data mining: Classification and prediction
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule mining, and ensemble methods like bagging and boosting. Classification involves predicting categorical labels while prediction predicts continuous values. Key steps for preparing data include cleaning, transformation, and comparing different methods based on accuracy, speed, robustness, scalability, and interpretability.
Data Mining: Classification and analysis
Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance considers algorithm efficiency, scalability, and handling diverse and complex data types from multiple sources.
Introduction to Data Mining
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
multi dimensional data model
The document discusses multidimensional databases and data warehousing. It describes multidimensional databases as optimized for data warehousing and online analytical processing to enable interactive analysis of large amounts of data for decision making. It discusses key concepts like data cubes, dimensions, measures, and common data warehouse schemas including star schema, snowflake schema, and fact constellations.
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1.2 steps and functionalities
Data mining involves multiple steps in the knowledge discovery process including data cleaning, integration, selection, transformation, mining, and pattern evaluation. It has various functionalities including descriptive mining to characterize data, predictive mining for inference, and different mining techniques like classification, association analysis, clustering, and outlier analysis.
Data mining primitives
Data mining primitives include task-relevant data, the kind of knowledge to be mined, background knowledge such as concept hierarchies, interestingness measures, and methods for presenting discovered patterns. A data mining query specifies these primitives to guide the knowledge discovery process. Background knowledge like concept hierarchies allow mining patterns at different levels of abstraction. Interestingness measures estimate pattern simplicity, certainty, utility, and novelty to filter uninteresting results. Discovered patterns can be presented through various visualizations including rules, tables, charts, and decision trees.
Data mining tasks
This document discusses data mining and different types of data mining techniques. It defines data mining as the process of analyzing large amounts of data to discover patterns and relationships. The document describes predictive data mining, which makes predictions based on historical data, and descriptive data mining, which identifies patterns and relationships. It also discusses classification, clustering, time-series analysis, and data summarization as specific data mining techniques.
Data Mining: Application and trends in data mining
This document provides an overview of application trends in data mining. It discusses how data mining is used for financial data analysis, customer analysis in retail and telecommunications, biological data analysis, scientific research, intrusion detection, and more. It also outlines statistical and visualization techniques used in data mining as well as privacy and security considerations. The document concludes by encouraging the reader to explore additional self-help tutorials on data mining tools and techniques.
Data mining: Classification and prediction
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule mining, and ensemble methods like bagging and boosting. Classification involves predicting categorical labels while prediction predicts continuous values. Key steps for preparing data include cleaning, transformation, and comparing different methods based on accuracy, speed, robustness, scalability, and interpretability.
Data Mining: Classification and analysis
Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance considers algorithm efficiency, scalability, and handling diverse and complex data types from multiple sources.
Introduction to Data Mining
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
multi dimensional data model
The document discusses multidimensional databases and data warehousing. It describes multidimensional databases as optimized for data warehousing and online analytical processing to enable interactive analysis of large amounts of data for decision making. It discusses key concepts like data cubes, dimensions, measures, and common data warehouse schemas including star schema, snowflake schema, and fact constellations.
Clustering in Data Mining
Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.
web mining
Web mining uses data mining techniques to extract information from web documents and services. It involves web content mining of page content and search results, web structure mining of hyperlink structures, and web usage mining of server logs to find user access patterns. Data mining techniques like classification, clustering, and association rule mining can be applied to web data to discover useful patterns and information.
Web Mining & Text Mining
Web mining is the application of data mining techniques to extract knowledge from web data, including web content, structure, and usage data. Web content mining analyzes text, images, and other unstructured data on web pages using natural language processing and information retrieval. Web structure mining examines the hyperlinks between pages to discover relationships. Web usage mining applies data mining methods to server logs and other web data to discover patterns of user behavior on websites. Text mining aims to extract useful information from unstructured text documents using techniques like summarization, information extraction, categorization, and sentiment analysis.
Data Integration and Transformation in Data mining
This document summarizes key aspects of data integration and transformation in data mining. It discusses data integration as combining data from multiple sources to provide a unified view. Key issues in data integration include schema integration, redundancy, and resolving data conflicts. Data transformation prepares the data for mining and can include smoothing, aggregation, generalization, normalization, and attribute construction. Specific normalization techniques are also outlined.
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The document provides an overview of data mining concepts and techniques. It introduces data mining, describing it as the process of discovering interesting patterns or knowledge from large amounts of data. It discusses why data mining is necessary due to the explosive growth of data and how it relates to other fields like machine learning, statistics, and database technology. Additionally, it covers different types of data that can be mined, functionalities of data mining like classification and prediction, and classifications of data mining systems.
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Data preparation
Data preprocessing is crucial for data mining and includes data cleaning, integration, reduction, and discretization. The goals are to handle missing data, smooth noisy data, reduce inconsistencies, integrate multiple sources, and reduce data size while maintaining analytical results. Common techniques include filling in missing values, identifying outliers, aggregating data, feature selection, binning, clustering, and generating concept hierarchies to replace raw values with semantic concepts. Preprocessing addresses issues like dirty, incomplete, inconsistent data to produce high quality input for mining models and decisions.
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©2013 Han, Kamber & Pei. All rights reserved.
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Classification in data mining
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
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Data preprocessing
Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
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This document provides an introduction to data mining techniques. It discusses how data mining emerged due to the problem of data explosion and the need to extract knowledge from large datasets. It describes data mining as an interdisciplinary field that involves methods from artificial intelligence, machine learning, statistics, and databases. It also summarizes some common data mining frameworks and processes like KDD, CRISP-DM and SEMMA.
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Web mining is the application of data mining techniques to extract knowledge from web data, including web content, structure, and usage data. Web content mining analyzes text, images, and other unstructured data on web pages using natural language processing and information retrieval. Web structure mining examines the hyperlinks between pages to discover relationships. Web usage mining applies data mining methods to server logs and other web data to discover patterns of user behavior on websites. Text mining aims to extract useful information from unstructured text documents using techniques like summarization, information extraction, categorization, and sentiment analysis.
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This document summarizes key aspects of data integration and transformation in data mining. It discusses data integration as combining data from multiple sources to provide a unified view. Key issues in data integration include schema integration, redundancy, and resolving data conflicts. Data transformation prepares the data for mining and can include smoothing, aggregation, generalization, normalization, and attribute construction. Specific normalization techniques are also outlined.
01 Data Mining: Concepts and Techniques, 2nd ed.
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Data Mining
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
Data preparation
Data preprocessing is crucial for data mining and includes data cleaning, integration, reduction, and discretization. The goals are to handle missing data, smooth noisy data, reduce inconsistencies, integrate multiple sources, and reduce data size while maintaining analytical results. Common techniques include filling in missing values, identifying outliers, aggregating data, feature selection, binning, clustering, and generating concept hierarchies to replace raw values with semantic concepts. Preprocessing addresses issues like dirty, incomplete, inconsistent data to produce high quality input for mining models and decisions.
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The document discusses data preprocessing techniques. It explains that data preprocessing is important because real-world data is often noisy, incomplete, and inconsistent. The key techniques covered are data cleaning, integration, reduction, and transformation. Data cleaning handles missing values, noise, and outliers. Data integration merges data from multiple sources. Data reduction reduces data size through techniques like dimensionality reduction. Data transformation normalizes and aggregates data to make it suitable for mining.
Data Mining: Concepts and Techniques chapter 07 : Advanced Frequent Pattern M...
the slides contain:
Pattern Mining: A Road Map
Pattern Mining in Multi-Level, Multi-Dimensional Space
Constraint-Based Frequent Pattern Mining
Mining High-Dimensional Data and Colossal Patterns
Mining Compressed or Approximate Patterns
Sequential Pattern Mining
Graph Pattern Mining
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
©2013 Han, Kamber & Pei. All rights reserved.
Database Lecture Notes
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
introduction to data mining tutorial
Data Mining: Concepts and Techniques — Chapter 1 — — Introduction ,Jiawei Han and Micheline Kamber
Department of Computer Science
University of Illinois at Urbana-Champaign
www.cs.uiuc.edu/~hanj
©2006 Jiawei Han and Micheline Kamber. All rights reserved.
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This document discusses sockets programming in Java. It covers server sockets, which listen for incoming client connections, and client sockets, which connect to servers. It describes how to create server and client sockets in Java using the ServerSocket and Socket classes. Examples are provided of simple Java programs to implement a TCP/IP server and client using sockets.
Classification in data mining
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Introduction to Data Mining
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.
Knowledge discovery thru data mining
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.
Data preprocessing
Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
Object Based Databases
This presentation provides a summary of chapter 22 of book "Database System Concepts" by Abraham Silberschatz and et al.
3. mining frequent patterns
The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.
Big data visualization
This presentation have the concept of Big data.
Why Big data is important to the present world.
How to visualize big data.
Steps for perfect visualization.
Visualization and design principle.
Also It had a number of visualization method for big data and traditional data.
Advantage of Visualization in Big Data
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- 1. Major Issues in Data Mining V. Saranya AP/CSE Sri Vidya College of Engineering & Technology, Virudhunagar
- 3. • Issues – Mining Methodology – User interaction – Performance – Data types.
- 4. Mining Methodology & User Interaction Issues 1. Mining different kinds of knowledge in database. Different users-different knowledge-different way (with same database)
- 5. 2. Interactive Mining of knowledge at multiple levels of abstraction. Focus the search patterns. Different angles.
- 6. 4. Data mining query languages and ad hoc data mining High level data mining query language Conditions and constraints.
- 7. 3. Incorporation of background knowledge. Background & Domain knowledge.
- 8. 5. Presentation and visualization of data mining results. Use visual representations. Expressive forms like graph, chart, matrices, curves, tables, etc… 6. Handling noisy or incomplete data. Confuse the process Over fit the data (apply any outlier analysis, data cleaning methods) 7.Pattern evaluation- the interestingness problem. Pattern may be uninteresting to the user. Solve by user specified constraints.
- 9. Performance Issues • Efficiency and scalability of data mining algorithms. Running time. Should be opt for huge amount of data. • Parallel, Distributed and incremental mining algorithms. Huge size of database Wide distribution of data High cost Computational complexity Data mining methods Solve by; efficient algorithms.
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