Knowledge Discovery in Databases

Unit V: Dimensional Database Design

Database Systems

MTech First Semester

Dr. Mohsin Dar

Assistant Professor

Cloud & Software Operations Cluster

UPES

What is Knowledge Discovery in Databases (KDD)?

Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.

Key Characteristics

Non-trivial: Requires computational methods and algorithms beyond simple queries
Valid: Patterns should hold on new data with sufficient certainty
Novel: Previously unknown patterns and insights
Potentially Useful: Actionable information for decision-making
Understandable: Results should be interpretable by humans

KDD vs Data Mining: Understanding the Difference

KDD (Process)

Complete end-to-end process
Includes data preparation, selection, cleaning
Encompasses interpretation and evaluation
Iterative and interactive
Broader scope

Data Mining (Step)

One step within KDD
Focuses on pattern discovery
Applies specific algorithms
Core analytical phase
Narrower focus

Key Insight: Data Mining is the heart of KDD, but KDD includes all the preparatory and post-processing steps necessary for successful knowledge discovery.

The KDD Process: A Step-by-Step Journey

1

Data Selection

Identify and retrieve relevant data from various sources

2

Data Preprocessing

Clean, handle missing values, remove noise

3

Data Transformation

Normalize, aggregate, and reduce dimensions

4

Data Mining

Apply algorithms to discover patterns

5

Interpretation

Evaluate and interpret discovered patterns

6

Knowledge

Deploy actionable insights and decisions

Data Preprocessing: Foundation of Quality KDD

Why Preprocessing Matters

Real-world data is often incomplete, noisy, and inconsistent. Preprocessing can consume 60-80% of the KDD effort but dramatically improves results.

Key Preprocessing Tasks

Data Cleaning

Fill missing values
Smooth noisy data
Identify/remove outliers
Resolve inconsistencies

Data Integration

Combine multiple sources
Resolve schema conflicts
Handle redundancy
Entity resolution

Data Reduction

Dimensionality reduction
Numerosity reduction
Data compression
Feature selection

Data Transformation

Normalization
Aggregation
Generalization
Attribute construction

Core Data Mining Techniques

Classification

Predict categorical class labels. Examples: Decision Trees, Neural Networks, SVM, Naive Bayes

Clustering

Group similar objects together. Examples: K-Means, Hierarchical, DBSCAN

Association Rules

Discover interesting relationships. Examples: Apriori, FP-Growth algorithms

Regression

Predict continuous values. Examples: Linear, Polynomial, Logistic Regression

Anomaly Detection

Identify unusual patterns. Used in fraud detection, network security

Sequential Patterns

Find patterns in sequence data. Examples: Time series analysis, web log mining

Classification: Supervised Learning

Definition: Classification is the process of finding a model that describes and distinguishes data classes for predicting the class of new objects.

Classification Process

Training Phase

↓

Build Model using Training Data

↓

Validate Model Accuracy

↓

Testing Phase

↓

Apply Model to New Data

Popular Algorithms

Decision Trees: IF-THEN rules in tree structure
Random Forest: Ensemble of decision trees
Neural Networks: Inspired by biological neurons
Support Vector Machines: Find optimal hyperplane

Clustering: Unsupervised Learning

Definition: Clustering groups data objects based on similarity without predefined class labels. Objects in the same cluster are similar; objects in different clusters are dissimilar.

Clustering Methods

Partitioning: K-Means, K-Medoids
Hierarchical: Agglomerative, Divisive
Density-based: DBSCAN, OPTICS
Grid-based: STING, CLIQUE

Applications

Customer segmentation
Image segmentation
Document organization
Anomaly detection
Gene analysis

K-Means Algorithm: Most popular partitioning method. Assigns each object to the cluster with nearest centroid. Iteratively refines clusters until convergence.

Association Rule Mining

Definition: Finding frequent patterns, associations, or causal structures among sets of items in transaction databases.

Key Concepts

Support

Frequency of occurrence of an itemset in the database

Support(A→B) = P(A∪B)

Confidence

Probability that B occurs when A occurs

Confidence(A→B) = P(B|A)

Classic Example: Market Basket Analysis

Rule: {Milk, Bread} → {Butter}
Interpretation: Customers who buy milk and bread also tend to buy butter
Support: 30% of transactions | Confidence: 75%

Apriori Algorithm

Most influential algorithm for mining frequent itemsets. Uses a level-wise search strategy with candidate generation.

Real-World Applications of KDD

🏢 Business Intelligence

Customer profiling, market basket analysis, churn prediction, sales forecasting

💳 Finance

Credit scoring, fraud detection, risk assessment, algorithmic trading

🏥 Healthcare

Disease diagnosis, treatment effectiveness, patient monitoring, drug discovery

🛒 E-commerce

Recommendation systems, dynamic pricing, inventory optimization, customer segmentation

📱 Social Media

Sentiment analysis, trend detection, influence analysis, content recommendation

🔒 Security

Intrusion detection, malware classification, network monitoring, threat intelligence

Challenges and Considerations in KDD

Technical Challenges

Scalability: Handling massive datasets (Big Data) efficiently
High Dimensionality: Curse of dimensionality in feature-rich datasets
Heterogeneous Data: Integrating structured, semi-structured, and unstructured data
Data Quality: Dealing with incomplete, noisy, and inconsistent data
Real-time Processing: Stream mining and online learning requirements

Methodological Challenges

Overfitting: Model performs well on training but poorly on new data
Feature Selection: Identifying relevant attributes from thousands of features
Model Selection: Choosing appropriate algorithms for the problem
Evaluation: Measuring effectiveness of discovered patterns

Ethical and Legal Considerations

Privacy: Protecting sensitive personal information
Bias: Ensuring fairness and avoiding discrimination
Transparency: Explainability of AI/ML decisions

Summary and Future Directions

Key Takeaways

                KDD is a complete process; data mining is one crucial step
Data preprocessing is critical and time-consuming (60-80% effort)
Multiple techniques: Classification, Clustering, Association Rules, etc.
Wide range of real-world applications across industries
Significant technical and ethical challenges remain

            

Emerging Trends

Deep Learning

Advanced neural networks for complex pattern recognition

AutoML

Automated machine learning pipeline optimization

Graph Databases

Efficient handling of connected data relationships

Edge Computing

Data processing closer to the source

Blockchain

Secure and transparent data transactions

AI Ethics

Responsible AI and data governance