Book Details

Data Ware housing and Mining

Data Ware housing and Mining

Published by uLektz

Course Code : ULZ0304
Author : uLektz
University : General for All University
Regulation : 2017
Categories : Computer Science
Format : ico_bookePUB3 (DRM Protected)
Type :



Buy Now

Description :Data Ware housing and Mining of ULZ0304 covers the latest syllabus prescribed by General for All University for regulation 2017. Author: uLektz, Published by uLektz Learning Solutions Private Limited.

Note : No printed book. Only ebook. Access eBook using uLektz apps for Android, iOS and Windows Desktop PC.

UNIT – I: Introduction to data mining

1.1 Introduction - What Motivated Data Mining? - Why Is It Important - Data Mining - On What Kind of Data

1.2 Data Mining Functionalities - What Kinds of Patterns Can Be Mined? - Are All of the Patterns Interesting?

1.3 Classification of Data Mining Systems

1.4 Data Mining Task Primitives

1.5 Integration of a Data Mining System with a Database or Data Warehouse System

1.6 Major Issues in Data Mining.

UNIT – II:Data Pre-processing

2.1 Data Pre-processing - Why Pre-process the Data? - Descriptive Data Summarization

2.2 Data Cleaning

2.3 Data Integration and Transformation

2.4 Data Reduction - Data Discretization and Concept Hierarchy Generation

UNIT – III: Data Warehouse and OLAP Technology:

3.1 Data Warehouse and OLAP Technology - An Overview : What Is a Data Warehouse?

3.2 A Multidimensional Data Model

3.3 Data Warehouse Architecture

3.4 Data Warehouse Implementation

3.5 From Data Warehousing to Data Mining

UNIT – IV: Introduction to decision trees

4.1 Classification : Basic Concepts - General Approach to solving a classification problem

4.2 Decision Tree Induction - Working of Decision Tree - Building a decision tree - Methods for expressing an attribute test conditions - Measures for selecting the best split, Algorithm for decision tree induction.

4.3 Model Over fitting - Due to presence of noise, due to lack of representation samples

4.4 Evaluating the performance of classifier - Holdout method - random sub sampling - Cross-validation - Bootstrap.

UNIT – V: Association Analysis

5.1 Association Analysis: Basic Concepts and Algorithms - Introduction

5.2 Frequent Item Set generation

5.3 Rule generation

5.4 Compact representation of frequent item sets

5.5 FP-Growth Algorithm

UNIT – VI: Cluster Analysis, K-means

6.1 Cluster Analysis: Basic Concepts and Algorithms - What Is Cluster Analysis? - Different Types of Clustering - Different Types of Clusters

6.2 K-means - The Basic K-means Algorithm - K-means - Additional Issues - Bisecting Kmeans - K-means and Different Types of Clusters - Strengths and Weaknesses - K-means as an Optimization Problem

6.3 Agglomerative Hierarchical Clustering - Basic Agglomerative Hierarchical Clustering Algorithm - Specific Techniques

6.4 DBSCAN - Traditional Density: Center-Based Approach - The DBSCAN Algorithm - Strengths and Weaknesses.