Data Mining and Preprocessing Techniques

• Study notes on data mining and preprocessing techniques

Introduction to Data Mining

• Data mining extracts patterns and insights from large datasets
• The KDD process involves data cleaning, integration, transformation, mining, pattern evaluation, and knowledge presentation

The Knowledge Discovery in Databases (KDD) Process

• Data cleaning removes noise and inconsistent information from large datasets
• Data integration combines multiple data sources into a unified dataset
• Data transformation converts data into a suitable format for mining
• Data mining applies algorithms to extract patterns from preprocessed data
• Pattern evaluation assesses the value of discovered patterns
• Knowledge presentation visualizes and presents the extracted knowledge

Why Data Mining?

• Data mining is driven by the explosive growth of data from digital transformation
• It is used across fields like business, science, and everyday applications
• It helps discover valuable insights within massive datasets

What is Data Mining?

• Data mining is also known as knowledge discovery in databases (KDD)
• It extracts previously unknown, potentially useful patterns or knowledge from data
• It differs from simple query systems or expert systems by relying on data analysis

Kinds of Data Can be Mined

• Data sources include relational databases, data warehouses, and transactional databases
• Data sources also include data streams, time-series data, spatial data, multimedia, and web data

What Kinds of Patterns Can be Mined?

• Generalization summarizes data characteristics
• Association and correlation analysis discovers frequent itemsets and correlations
• Classification and clustering builds models to classify data or group similar data points
• Outlier analysis identifies data points that deviate significantly from others

Data Objects and Attribute Types

• Data objects represent entities in the dataset like customers or products
• Attributes describe characteristics of data objects
• Attributes can be nominal, binary, ordinal, or numeric

Basic Statistical Descriptions of Data

• Common measures include mean, median, mode, variance, and standard deviation
• Statistical descriptions can measure a data's central tendency and variation

Data Quality Improvement

• Preprocessing enhances the quality of data used in data mining
• Accuracy ensures data correctness, preventing errors from instruments or human mistakes
• Completeness ensures all necessary data is present
• Consistency ensures data uniformity across sources
• Timeliness ensures data is updated and synchronized
• Believability ensures the trustworthiness of data
• Interpretability ensures ease of understanding

Data Cleaning

• Cleaning routines address incomplete, noisy, and inconsistent data
• Missing data is handled by ignoring samples, filling manually, or using constants, mean values, or statistical models
• Noisy data is managed via binning, regression, clustering, or human inspection

Data Integration

• Data integration combines data from multiple sources such as databases, files, and data cubes
• Schema integration unifies different data schemas
• Entity identification ensures consistency in identifying the same real-world entities from multiple sources

Handling Redundancy in Data Integration

• Redundant data becomes apparent through correlation analysis
• The Chi-square test is performed for nominal data attributes
• Correlation coefficients are used for numeric attributes

Preprocessing with WEKA

• WEKA offers machine learning algorithms and preprocessing techniques without programming
• It helps visualize, transform, train models, and evaluate results

Data Reduction Strategies

• Data reduction shrinks dataset size without losing critical information
• Techniques include dimensionality reduction, numerosity reduction, and data compression

Wavelet Transform

• Decomposes data into frequency sub-bands, preserving details at various resolutions

Principal Component Analysis (PCA)

• Reduces dimensionality by transforming data into principal components
• These transformations capture the most variance

Attribute Subset Selection

• Attribute subset selection improve model accuracy
• This is achieved by selecting relevant features and discarding redundant ones

Numerosity Reduction

• Reduces data volume using parametric or non-parametric methods
• Regression methods are a type of parametric method
• Histograms and clustering are types of non-parametric methods

Sampling

• Sampling means to select a representative subset of the data for analysis
• Stratified sampling ensures balanced representation of different categories

Data Transformation and Normalization

• Methods like aggregation, smoothing, and scaling prepare data for mining
• Aggregation is the process of gathering and expressing data in a summary form
• Normalization scales data to specific ranges, facilitating comparison across attributes

Discretization

• Discretization converts continuous attributes into discrete bins using binning and clustering

Measuring Data Similarity and Dissimilarity

• Similarity and dissimilarity can be used to evaluate how similar or different objects are
• Euclidean distance is a function that measures the data similarity of points

Frequent Pattern Analysis

• Frequent pattern analysis discovers recurring patterns within datasets

Association Rule Mining

• Association rule mining generates rules based on support and confidence metrics

Closed Patterns and Max-Patterns

• Closed patterns ensures no loss of information
• Max-patterns identifies the largest frequent itemsets

Frequent Itemset Mining Algorithms

• The Apriori Algorithm iteratively trims infrequent patterns
• The FP-Growth Algorithm uses an FP-tree for efficient mining

Pattern Evaluation

• Additional metrics such as the lift and chi-square refine the significance of association rules

Pattern Mining in Multi-Level, Multi-Dimensional Space

• Multi-Level Association Rules: Items have hierarchical relationships
• Multi-Dimensional Association Rules: Incorporate multiple attributes like the age and location of an item or entity
• Quantitative Association Rules: Use numeric attributes with clustering or discretization methods

Mining Rare and Negative Patterns

• Rare Patterns: Low-support but interesting patterns such as an analysis of luxury purchases
• Negative Patterns: Relationships showing negative correlations between data points

Constraint-Based Mining

• Focuses on specific patterns using user-defined constraints, pruning irrelevant patterns

Handling High-Dimensional and Colossal Patterns

• Constrained FP-growth can be used to manage mining in large, complex datasets

Supervised vs. Unsupervised Learning

• Supervised Learning: Uses labeled data to predict outcomes
• Unsupervised Learning: Analyzes data without predefined labels, typically for clustering

Classification Process

• Training Phase: Build a model using labeled data
• Testing Phase: Evaluate the model on unseen data

Decision Tree Induction

• A flowchart-like structure where nodes represent attribute tests, branches represent outcomes, and leaves represent class labels
• Pruning reduces overfitting by removing low-impact branches

Attribute Selection Measures

• Metrics like Information Gain, Gain Ratio, and Gini Index help select the best attributes for data splits

Bayesian Classification

• Predicts class membership using Bayes’ theorem, assuming feature independence

Rule-Based Classification

• Uses IF-THEN rules to predict outcomes based on attribute conditions

Model Evaluation

• Metrics include accuracy, precision, recall, F1-score, and confusion matrices
• Techniques like cross-validation improve reliability