Data Mining Techniques, Applications, and Processes
1. Architecture of Data Mining
Data mining, a crucial process for extracting knowledge from large datasets, involves more than just extracting data. It’s about uncovering valuable insights. Think of it as “knowledge mining” rather than simply “data mining.”
The process includes these key steps:
- Data Cleaning: Removing noise and inconsistencies.
- Data Integration: Combining multiple data sources.
- Data Selection: Retrieving relevant data for analysis.
- Data Transformation: Converting data into suitable formats for mining (e.g., aggregation).
- Data Mining: Applying intelligent methods to extract patterns.
- Pattern Evaluation: Identifying interesting and valuable patterns.
- Knowledge Presentation: Representing mined knowledge using visualization techniques.
Steps 1 through 4 are data preprocessing stages, preparing the data for mining. The data mining step may involve user interaction or a knowledge base. Interesting patterns are presented to the user and can be stored as new knowledge.
4. Multidimensional Data Model
The multidimensional data model organizes data for efficient analysis and retrieval. Unlike relational databases that use queries, it allows users to explore analytical questions related to market or business trends. This model enables rapid answers by quickly creating and examining data.
OLAP
OLAP (Online Analytical Processing) and data warehousing utilize multidimensional databases to present data from multiple perspectives. Data cubes, defined by dimensions and facts, model and view data from various angles. Fact tables contain numerical measures related to dimensional tables.
Example: Consider a factory’s quarterly sales data in Bangalore:
5. Data Integration
Data integration combines data from multiple sources into a unified view. This involves cleaning, transforming, and resolving inconsistencies. The goal is to enhance data usability for analysis and decision-making. Techniques include data warehousing, ETL (Extract, Transform, Load), and data federation.
Formally, data integration is defined as a triple (G, S, M):
- G: Global schema
- S: Heterogeneous source schemas
- M: Mappings between source and global schema queries
6. Data Classification
Bayesian Classification
Bayesian classification utilizes Bayes’ theorem to predict event occurrences. Bayesian classifiers are statistical classifiers based on Bayesian probability. The theorem expresses belief as probability.
Bayes’ Theorem: P(H|X) = [P(X|H) * P(H)] / P(X), where X and H are events, and P(X) ≠ 0.
- P(X|H): Conditional probability of X given H.
- P(H|X): Conditional probability of H given X.
- P(H), P(X): Prior probabilities of H and X occurring independently.
Bayes’ Theorem calculates the probability of an event based on prior knowledge of related conditions. It uses conditional probability to create an algorithm that calculates limits on an unknown parameter using evidence.
Types of Probabilities:
- Prior Probability [P(H)]
- Posterior Probability [P(H|X)]
- X: Data tuple
- H: Hypothesis
Data Cleaning
Data cleaning is crucial for accurate and reliable datasets. It involves a systematic process to identify and correct errors, inconsistencies, and inaccuracies.
Steps:
- Removal of Unwanted Observations: Eliminate irrelevant or duplicate records.
- Fixing Structural Errors: Address inconsistencies in formats, naming, or variable types.
- Managing Unwanted Outliers: Identify and manage data points significantly deviating from the norm.
- Handling Missing Data: Impute missing values or remove records with missing data.
7. Apriori Algorithm
The Apriori algorithm calculates association rules between objects, revealing how they relate to each other. It’s a frequent pattern mining algorithm that analyzes relationships like “customers who bought A also bought B.”
- Introduced by R. Agrawal and R. Srikant in 1994.
- Uses prior knowledge of frequent itemset properties (hence the name “Apriori”).
- Employs an iterative, level-wise search.
- Uses the Apriori property to improve efficiency by reducing the search space.
Example: Product combos in stores (e.g., pizza, soft drink, breadsticks) are based on association rules discovered through techniques like the Apriori algorithm.
8. Cluster Analysis Applications
Cluster analysis has diverse applications:
- Marketing: Customer segmentation.
- Biology: Species classification.
- Libraries: Book categorization.
- Insurance: Customer and policy analysis, fraud detection.
- City Planning: Grouping houses and studying their values.
- Earthquake Studies: Identifying dangerous zones.
- Image Processing: Grouping similar images, content-based classification.
- Genetics: Grouping genes with similar expression patterns.
- Finance: Market segmentation, stock market pattern analysis, risk assessment.
- Customer Service: Categorizing inquiries and complaints.
9. Hierarchical Clustering
Hierarchical clustering builds a hierarchy (tree-like structure) of clusters. It’s a connectivity-based model that groups data points based on similarity or distance, assuming closer points are more related.
Types of Hierarchical Clustering:
- Agglomerative Clustering (bottom-up)
- Divisive Clustering (top-down)
Hierarchical clustering is one of several clustering algorithms used in machine learning, including connectivity-based, centroid-based, distribution-based, and density-based approaches.