Data mining 1 - Introduction (cheat sheet - printable)
- 1. Data Mining Lecture 1 Why Data Mining? Negative Points All sounds complicated. Why should I learn about Data Mining? What's wrong with just relational databases? Why would I want to go through these extra complicated steps? Isn't it expensive? It sounds like it takes a lot of skills, programming, computational time and storage space. Where's the benefit? Positive Points Data Mining is not just a cute academic exercise It has very profitable real world uses Practically all large companies and manygovernments perform data mining as part of their planning and analysis Data Explosion Data explosion problem Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories We are drowning in data, but starving for knowledge! Solution: Data Mining Extraction of interesting knowledge (rules, regularities, patterns, constraints) from large datasets What is Data Mining? Data mining (knowledge discovery in databases): Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases Alternative names and their “inside stories”: Data mining: a misnomer? Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archaeology, data dredging, information harvesting, business intelligence, etc. What is not data mining? (Deductive) query processing. ● Expert systems or small ML/statistical programs Potential Applications Database analysis and decision support Market analysis and management o target marketing, customer relation management, market basket analysis, cross selling, market segmentation Risk analysis and management o Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and management Other Applications Text mining (news group, email, documents) and Web analysis Intelligent query answering Market Analysis Where are the data sources for analysis? Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies Target marketing Find clusters or “model” customers who share the same characteristics: interest, income level, spending habits, etc. Determine customer purchasing patterns over time Conversion of single to a joint bank account: marriage, etc. Cross-market analysis Associations/correlations between product sales Prediction based on the association information
- 2. Corporations Finance planning and asset evaluation cash flow analysis and prediction contingent claim analysis to evaluate assets cross-sectional and time series analysis (financialratio, trend analysis, etc.) Resource planning summarize and compare the resources and spending Competition monitor competitors and market directions group customers into classes and a class- based pricing procedure set pricing strategy in a highly competitive market Market Management Customer profiling data mining can tell you what types of customers buy what products (clustering or classification) Identifying customer requirements identifying the best products for different customers use prediction to find what factors will attract new customers Provides summary information various multidimensional summary reports statistical summary information (data central tendency and variation) Fraud Detection Applications widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc. Approach use historical data to build models of fraudulent behaviour and use data mining to help identify similar instances Examples auto insurance: detect a group of people who stage accidents to collect on insurance money laundering: detect suspicious money transactions (US Treasury's Financial Crimes Enforcement Network) medical insurance: detect professional patients and ring of doctors and ring of references Detecting inappropriate medical treatment Australian Health Insurance Commission identifies that in many cases blanket screening tests were requested (save Australian €1m/yr) Detecting telephone fraud Telephone call model: destination of the call, duration, time of day or week. Analyse patterns that deviate from an expected norm British Telecom identified discrete groups of callers with frequent intra-group calls, especially mobile phones, and broke a multimillion dollar fraud Retail Analysts estimate that 38% of retail shrink is due to dishonest employees Other Applications Sports IBM Advanced Scout analysed NBA game statistics (shots blocked, assists, and fouls) to gain competitive advantage for New York Knicks and Miami Heat Astronomy JPL and the Palomar Observatory discovered 22 quasars with the help of data mining Internet Web Surf-Aid IBM Surf-Aid applies data mining algorithms to Web access logs for market-related pages to discover customer preference and behaviour pages, analysing effectiveness of Web marketing, improving Web site organisation, etc.
- 3. KDD Process Learning the application domain relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and pre-processing (may take 60% of effort!) Data reduction and transformation Find useful features, dimensionality/variable reduction, invariant representation Choosing functions of data mining summarisation, classification, regression, association, clustering Choosing the mining algorithms Data mining: search for patterns of interest Pattern evaluation and knowledge presentation visualisation, transformation, removing redundant patterns, etc. Use of discovered knowledge Business Intelligence Architecture of DM System DM Software Tools DM Software Tools We can find several Data Mining tools Commercial and free (open source) software tools NeuroOnline, ARMiner, Tminer, Bayda, Cluto, YALE,Rainbow, MDR, C5, Clementine, etc. RapidMiner Free version Commercial version