Mca535 data mining and data warehousing
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This document outlines the syllabus for an MCA535 Data Mining and Data Warehousing course. The course is divided into 5 units that cover topics such as data preprocessing, data warehousing and OLAP technology, frequent pattern mining, classification and prediction, cluster analysis, and applications of data mining. The first unit introduces data mining concepts and the integration of data mining systems with databases. Later units cover efficient methods for data cube computation, mining frequent itemsets and rules, classification techniques like decision trees and Bayesian classification, clustering methods, and mining time-series and spatial data. The syllabus concludes with applications and trends in data mining.
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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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lineage, data quality and populating transformed data into data warehouse. Spatial data warehouses are
based on spatial data mostly collected from Geographical Information Systems (GIS) and the transactional
systems that are specific to an application or enterprise. Metadata design and deployment is the most
critical phase in building of data warehouse where it is mandatory to bring the spatial information and
data modeling together. In this paper, we present a holistic metadata framework that drives metadata
creation for spatial data warehouse. Theoretically, the proposed metadata framework improves the
efficiency of accessing of data in response to frequent queries on SDWs. In other words, the proposed
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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 provides an overview of data mining concepts and techniques from the third edition of the textbook "Data Mining: Concepts and Techniques" by Jiawei Han, Micheline Kamber, and Jian Pei. It introduces why data mining is important due to the massive growth of data, defines data mining, and discusses the multi-dimensional nature of data mining including the types of data, patterns, techniques and applications. The chapter also covers data mining functions such as generalization, association analysis, classification, and cluster analysis.
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: 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.
DM course outlines.pdf
This document outlines the topics and structure of a 13-week course on data mining. The course introduces fundamental data mining concepts and techniques in the first 5 weeks, including data preprocessing, data warehousing, frequent pattern mining, and classification. The following weeks cover more advanced methods for clustering, outlier detection, and trend analysis. Key methods taught include decision trees, Bayes classification, k-means clustering, and proximity-based outlier detection. The document recommends textbooks on data mining concepts, techniques, applications, and research frontiers to supplement the course material.
Introduction.ppt
This document provides an introduction to data mining concepts and techniques. It discusses why data mining is needed due to the massive growth of data. Data mining involves extracting useful patterns from large datasets through techniques such as classification, clustering, association rule mining. It is an interdisciplinary field that draws from areas like machine learning, statistics, database systems and visualization. The document outlines key steps in the knowledge discovery process and issues in data mining like pattern evaluation and scalability.
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVE
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
Introduction to data warehouse
The document provides an introduction to the concept of data mining. It discusses the evolution of data analysis techniques from empirical to computational to data-driven approaches. Data mining is presented as a natural evolution to analyze massive data sets and discover useful patterns. Key aspects of data mining covered include its functionality, types of data and knowledge that can be mined, major issues, and its relationship to other fields such as machine learning, statistics, and databases.
Introduction
Data mining involves discovering interesting patterns from large amounts of data. It is an outgrowth of database technology that has wide applications. The data mining process includes data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. Data mining can operate on various data sources and provides techniques for characterization, classification, clustering, association analysis and other functions to discover useful knowledge from data.
Dwdmunit1 a
The document provides an overview of data mining and data warehousing concepts through a series of lectures. It discusses the evolution of database technology and data analysis, defines data mining and knowledge discovery, describes data mining functionalities like classification and clustering, and covers data warehouse concepts like dimensional modeling and OLAP operations. It also presents sample queries in a proposed data mining query language.
Similar to Mca535 data mining and data warehousing (20)
Data Mining: Concepts and techniques: Chapter 13 trend
Data Mining: Concepts and techniques: Chapter 13 trend
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVE
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVE
Mca535 data mining and data warehousing
- 1. MCA535 Data Mining and Data Warehousing Total teaching Hours/Semester: 60 No of Lecture Hours/Week: 04 Unit I. (12) Introduction What Is Data Mining? Data Mining—On What Kind of Data? Data Mining Functionalities, Classification of Data Mining Systems, Data Mining Task Primitives, Integration of a Data Mining System with a Database or DataWarehouse System, Major Issues in Data Mining. Data Preprocessing Why Preprocess the Data? Descriptive Data Summarization – Measuring the central tendency- Measuring the dispersion of data. Unit II. (12) Data Preprocessing (cont.) Data Cleaning-Missing Values-Noisy Data-Data Cleaning as a Process, Data Integration and Transformation, Data Reduction-Data Cube Aggregation-Attribute Subset Selection- Dimensionality Reduction-Numerosity Reduction. DataWarehouse and OLAP Technology What Is a DataWarehouse? A Multidimensional Data Model, DataWarehouse Architecture, DataWarehouse Implementation, From DataWarehousing to Data Mining. Unit III. (12) Data Cube Computation and Data Generalization Efficient Methods for Data Cube Computation – Road map - Multiway array aggregation – Star cubing, Further Development of Data Cube and OLAP Technology. Mining Frequent Patterns and Associations Basic Concepts, Efficient and Scalable Frequent Itemset Mining Methods – Apriori algorithm, Generating Rules – Improving efficiency – Mining frequent itemset without candidate generation. Unit IV. (14) Classification and Prediction What Is Classification? What Is Prediction? Issues Regarding Classification and Prediction, Classification by Decision Tree – Decision tree induction – Attribute selection, Bayesian Classification – Bayesian Theorem - naïve Bayesian, Rule-Based Classification, Prediction, Accuracy and Error Measures. Cluster Analysis What Is Cluster Analysis? Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods – K-Means and K-Medoids, Hierarchical Methods – Agglomerative and Divisive, Density Based Methods - DBSCAN, Outlier Analysis – Statistical based. Syllabus 2009 MCA 84 Christ University, Bangalore, India Unit V. (10) Mining Time-Series and Spatial Data Mining Time-Series Data – Trend analysis – Similarity search, Spatial Data Mining- Spatial Data Cube Construction and Spatial OLAP-Mining Spatial Association and Colocation Patterns-Spatial Clustering, Classification Methods-Mining Raster Databases Applications and Trends in Data Mining
- 2. Data Mining Applications, Data Mining System Products and Research Prototypes, Social Impacts of Data Mining. Text Book: 1. Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, USA, 2nd edition, 2006. Reference Books: 1. Claudia Imhoff, Nicholas & et al, Mastering Data warehouse Design, J Wiley, 2003 2. Berson A & Smith S J, Data warehousing, Data Mining & OLAP, Mc Graw Hall, 1997. 3. Margaret H. Dunham, Data mining-Introductory and Advanced topics Pearson Education, 2003 4. Inmon W H, Building the Data Warehouse, John Wiley & Sons, 3rd edition, 2005