data mining and knowledge discovery in large databases
DESCRIPTION
AACIMP 2011 Summer School. Operational Research Stream. Lecture by Erik Kropat.TRANSCRIPT
„We are drowning in data, but we are starving for knowledge“ Part 2: Clustering - Hierarchical Clustering - Divisive Clustering - Density based Clustering
Outline
Data Mining and
Knowledge Discovery in Large Databases
Erik Kropat University of the Bundeswehr
Munich, Germany
Why “Data Mining”?
• Companies are collecting massive amounts of data on customers, operations, and the competitive landscape.
Firms can gain a competitive advantage from these data
• But, there is far too much data
− Online shops record purchase behaviours for millions of customers (sometimes with hundreds features for each customer)
− Phone companies keep info on 100’s of millions of accounts (each with thousands of transactions)
− Databases can often be hundreds of terabytes in size (this will be peanuts in the future).
„We are drowning in data, but we are starving for knowledge“
Why “Data Mining”?
(John Naisbitt)
Process of finding valuable and useful patterns in datasets
Knowledge Discovery in Large Databases
… or more complex data sets • multimedia & sound
• images & video
• automatic news analysis
• social media analysis.
• businesses & investments
• finance & economics
• science & technology
• bioinformatics
• telecommunication
Analysis of data sets from …
What are the data sources?
− Credit card transactions data
− Supermarket transactions data
− Loyalty cards
− Web server logs
− Social media
Variety of features
− Name and address − History of shopping and purchases − Demographics − Credit rating − Quality & market share of products
Consumer data
Business Intelligence ‒
Customer Data Analytics & Market Analysis
− customer segmentation
− market basket analysis
− target marketing
− geo-marketing
− cross-selling / up-selling
− customer relation management
Market Basket Analysis ‒ Cross Selling
Key Tasks
Assocation Rule Learning
Decision Trees
Automatic Derivation of Ontologies
Neural Networks
Digital Forensics
Retail
• Customer segmentation Identify purchase patterns of „typical“ customers
Targeted advertisement, costumized pricing, cost-effective promotions • Market basket analysis Identify the purchase behaviour of groups of customers
• Sales promotions Identify likely responders to sales promotions
Banking
• Credit rating
Given a large number names, which persons are likely to default on their credit cards?
• Fraud detection
− Credit card fraud detection
− Network intrusion detection
Telecommunications
Companies are facing an escalating competition and are forced to aggressively market special pricing programs aimed at retaining existing customers and attracting new ones. • Call detail record analysis Identify customer segments with similar use patterns.
Offer attractive pricing and feature promotions. • Customer loyalty / customer churn management Some customers repeatedly „churn“ (switch providers).
Identify those who are likely to switch or who are likely to remain loyal.
Companies can target their spending on customers who will produce the most profit. • Set pricing strategies in a highly competitive market.
Big Data is Big Business Companies are using their data sets to aim their services and products with increasing precision.
Business Intelligence
− SAP AG is a German global software corporation that provides enterprise software applications.
− SAP AG is one of the largest enterprise software companies. − In October 2007, SAP AG announced a $6.8 billion deal to acquire „Business Objects“.
− Since 2009 „Business Objects“ is a division of SAP AG instead of a separate company.
Outline
Part 1: Introduction - What is „Data Mining“ ? - Examples
Outline
Part 3: Clustering - Hierarchical Clustering - Partitional Clustering - Fuzzy Clustering - Graph Based Clustering
Part 4: Classification - k-th Nearest Neighbors - Support Vector Machines
Part 2: Formal Concept Analysis - Contexts and Concepts - Concept Lattices
Part 5: Spatial Data Mining - DBSCAN - Density & Connectivity
Part 6: Regulatory Networks - Eco-Finance Networks - Gene-Environment Networks