Data Mining Concepts

Lecture 1 Recap

• Topics covered include black-box concept, data mining motivation, evolution of sciences, database technology evolution, knowledge discovery, data mining and business intelligence, KDD process from ML and statistics, and data mining tasks, plus a summary and checklist.

Lecture 2 Content

• This lecture includes concepts like class description, classification, cluster analysis, association and correlation analysis, sequential pattern analysis, outlier analysis, time-series mining, structure and network analysis, knowledge evaluation, used technologies, data mining applications, and major issues, with a summary and checklist.

Data Mining Tasks

• There are two primary types of data mining tasks: descriptive and predictive.
• Descriptive mining characterizes data properties within a target dataset.
• Predictive mining uses current data to make future predictions through induction.
• Data mining functionalities categorize patterns to be found, including:
  • Classification tasks, which are predictive.
  • Mining of frequent patterns, descriptive in nature.
  • Regression tasks for prediction.
  • Descriptive clustering analysis.
  • Predictive outlier analysis.

Data Mining Functionalities

• Data is linked with classes or concepts.
• Class/Concept descriptions describe individual classes and concepts precisely and concisely.
• Data characterization summarizes general characteristics or features of a target class of data.
• Data Discrimination compares a target class with comparative (contrasting) classes.
• Statistical measures and data cube-based OLAP tools are used.
• Outputs present data in charts, curves, and multidimensional data cubes.

Output and Examples

• Output is similar to characterization but includes comparative measures.
• Example: A company can compare customers who shop for computer products regularly versus those who rarely do.

Classification

• The process involves training data where information is labeled and learning the data's features.
• A model is built, then testing data is used to evaluate its functionality.
• Lastly, the model is applied to unlabeled data to predict outcomes.

Key Aspects

• Classification is a label prediction process.
• Models (functions) are constructed using data with known class labels.
• Future predictions can be made by distinguishing classes or concepts, such as classifying countries by climate or cars by gas mileage.
• Predicting unknown class labels is a key goal, and methods like decision trees and neural networks are used.

Examples of Application

• Credit card fraud detection.
• Direct marketing.
• Classifying stars, diseases, and web pages.

Cluster Analysis

• Cluster analysis groups ungrouped data by analyzing features and identifying similarities.
• The objective is to find the best data grouping or clustering.

Goal

• Data objects are analyzed and clustered without using class labels
• Data is categorized into new clusters to find distribution patterns
• Clustering is based on maximizing intraclass similarity and minimizing interclass similarity, with customer segmentation as an example

Additional Information

• The goal is to divide a market into distinct customer subsets for targeted marketing.

Association and Pattern Analysis

• This involves identifying patterns that frequently occur in data, whether they are itemsets, subsequences, or substructures.
• Mining such patterns helps discover interesting associations and correlations within the data.
• Frequent item-sets appear together or show frequently occurring subsequences.
• Example: Customers tend to purchase a laptop first, followed by a digital camera and then a memory card.

Association Rules

• Association rules demonstrate relationships, for instance, "Buys(X, 'bread') implies buys(X, 'milk’) [support = 50%, confidence = 75%]."

Outlier Analysis

• Outlier analysis detects data that deviates from the norm.
• It's utilized in fraud detection and rare events analysis.
• An outlier is a data object significantly different from the general data behavior and can be spotted by uncovering fraudulent credit card usage and spotting large, irregular payments.

Time Series Mining

• Time Series are time-ordered observations where data is collected at constant intervals, charting changes over time
• Time series analysis identifies time-based patterns to forecast future behaviors

Graph and Internet Analysis

• Focuses on graph mining, which includes finding frequent subgraphs, such as chemical compounds, within networks.
• Involves analyzing relational aspects (edges) between actors (nodes),
• Networks can provide semantic information like web analysis.

Knowledge Evaluation

• Mined knowledge is considered interesting if it's easily understood and validated on new test data.
• The knowledge must also be potentially useful and novel
• A mined pattern validating sought confirmations is deemed interesting and represents knowledge

Tech used in Data Mining

• Machine Learning
• Pattern Recognition
• Statistics
• Applications
• Visualization
• Algorithms
• Database tech
• High performance computing

Data Mining Applications

• Where there is data, there are data mining applications
• Web page analysis.
• Collaborative analysis, recommender systems.
• Basket data analysis for targeted marketing.
• Biological and medical data analysis.
• Software engineering.

Major Issues in Data Mining

• Mining diverse and new types of knowledge in multidimensional space requires an interdisciplinary effort.
• Data can be handled with noise, uncertainty, and incompleteness through pattern evaluation and constraint-guided mining.
• In this model User interaction can include interactive mining, background knowledge incorporation, and the presentation/visualization of data mining results

Scalability & Data

• Data mining algorithms must be highly scalable and efficient.
• Data mining should be able to handle complex types of data.
• Consider how data repositories can be dynamic, global, and networked.

Data in Society

• Social impacts, preserving privacy, and invisible data mining are all factors that should be considered with Data Mining
• Data mining should discover interesting patterns from massive amounts of data.
• It requires data cleaning, integration, selection, transformation, evaluation, and knowledge presentation.
• Data mining includes characterization, discrimination, association, classification, clustering, outlier and trend analysis.