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Page-1

- Data mining - ( 3 - 14 )
- Data Warehouse - ( 15 - 21 )
- OLAP - ( 22 - 24 )
- Data Preprocessing - ( 25 - 27 )
- Association Rule Mining - ( 28 - 33 )
- EfficientEfficient Frequent Itemset Mining Methods - ( 34 - 43 )
- Classification and Prediction - ( 44 - 49 )
- Bayesian Classification - ( 50 - 52 )
- Classification by Back Propagation - ( 53 - 59 )
- Regression Analysis - ( 60 - 63 )
- Cluster Analysis - ( 64 - 76 )
- Outlier Analysis - ( 77 - 84 )

Topic:

LECTURE NOTES ON
DATA MINING& DATA WAREHOUSING
COURSE CODE:BCS-403
DEPT OF CSE & IT
VSSUT, Burla

SYLLABUS:
Module – I
Data Mining overview, Data Warehouse and OLAP Technology,Data Warehouse Architecture,
Stepsfor the Design and Construction of Data Warehouses, A Three-Tier Data
WarehouseArchitecture,OLAP,OLAP queries, metadata repository,Data Preprocessing – Data
Integration and Transformation, Data Reduction,Data Mining Primitives:What Defines a Data
Mining Task? Task-Relevant Data, The Kind of Knowledge to be Mined,KDD
Module – II
Mining Association Rules in Large Databases, Association Rule Mining, Market
BasketAnalysis: Mining A Road Map, The Apriori Algorithm: Finding Frequent Itemsets Using
Candidate Generation,Generating Association Rules from Frequent Itemsets, Improving the
Efficiently of Apriori,Mining Frequent Itemsets without Candidate Generation, Multilevel
Association Rules, Approaches toMining Multilevel Association Rules, Mining
Multidimensional
Association
Rules
for
Relational
Database
and
Data
Warehouses,Multidimensional Association Rules, Mining Quantitative Association Rules,
MiningDistance-Based Association Rules, From Association Mining to Correlation Analysis
Module – III
What is Classification? What Is Prediction? Issues RegardingClassification and Prediction,
Classification by Decision Tree Induction, Bayesian Classification, Bayes Theorem, Naïve
Bayesian Classification, Classification by Backpropagation, A Multilayer Feed-Forward Neural
Network, Defining aNetwork Topology, Classification Based of Concepts from Association Rule
Mining, OtherClassification Methods, k-Nearest Neighbor Classifiers, GeneticAlgorithms,
Rough Set Approach, Fuzzy Set Approachs, Prediction, Linear and MultipleRegression,
Nonlinear Regression, Other Regression Models, Classifier Accuracy
Module – IV
What Is Cluster Analysis, Types of Data in Cluster Analysis,A Categorization of Major
Clustering Methods, Classical Partitioning Methods: k-Meansand k-Medoids, Partitioning
Methods in Large Databases: From k-Medoids to CLARANS, Hierarchical Methods,
Agglomerative and Divisive Hierarchical Clustering,Density-BasedMethods, Wave Cluster:
Clustering Using Wavelet Transformation, CLIQUE:Clustering High-Dimensional Space,
Model-Based Clustering Methods, Statistical Approach,Neural Network Approach.
DEPT OF CSE & IT
VSSUT, Burla

Chapter-1
1.1 What Is Data Mining?
Data mining refers to extracting or mining knowledge from large amountsof data. The term is
actually a misnomer. Thus, data miningshould have been more appropriately named as
knowledge mining which emphasis on mining from large amounts of data.
It is the computational process of discovering patterns in large data sets involving methods at the
intersection of artificial intelligence, machine learning, statistics, and database systems.
The overall goal of the data mining process is to extract information from a data set and
transform it into an understandable structure for further use.
The key properties of data mining are
Automatic discovery of patterns
Prediction of likely outcomes
Creation of actionable information
Focus on large datasets and databases
1.2 The Scope of Data Mining
Data mining derives its name from the similarities between searching for valuable business
information in a large database — for example, finding linked products in gigabytes of store
scanner data — and mining a mountain for a vein of valuable ore. Both processes require either
sifting through an immense amount of material, or intelligently probing it to find exactly where
the value resides. Given databases of sufficient size and quality, data mining technology can
generate new business opportunities by providing these capabilities:
DEPT OF CSE & IT
VSSUT, Burla

Automated prediction of trends and behaviors. Data mining automates the process of finding
predictive information in large databases. Questions that traditionally required extensive handson analysis can now be answered directly from the data — quickly. A typical example of a
predictive problem is targeted marketing. Data mining uses data on past promotional mailings to
identify the targets most likely to maximize return on investment in future mailings. Other
predictive problems include forecasting bankruptcy and other forms of default, and identifying
segments of a population likely to respond similarly to given events.
Automated discovery of previously unknown patterns. Data mining tools sweep through
databases and identify previously hidden patterns in one step. An example of pattern discovery is
the analysis of retail sales data to identify seemingly unrelated products that are often purchased
together. Other pattern discovery problems include detecting fraudulent credit card transactions
and identifying anomalous data that could represent data entry keying errors.
1.3 Tasks of Data Mining
Data mining involves six common classes of tasks:
Anomaly detection (Outlier/change/deviation detection) – The identification of
unusual data records, that might be interesting or data errors that require further
investigation.
Association rule learning (Dependency modelling) – Searches for relationships
between variables. For example a supermarket might gather data on customer purchasing
habits. Using association rule learning, the supermarket can determine which products are
frequently bought together and use this information for marketing purposes. This is
sometimes referred to as market basket analysis.
Clustering – is the task of discovering groups and structures in the data that are in some
way or another "similar", without using known structures in the data.
Classification – is the task of generalizing known structure to apply to new data. For
example, an e-mail program might attempt to classify an e-mail as "legitimate" or as
"spam".
Regression – attempts to find a function which models the data with the least error.
DEPT OF CSE & IT
VSSUT, Burla

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