Presentation on theme: "OLAM and Data Mining: Concepts and Techniques. Introduction Data explosion problem: –Automated data collection tools and mature database technology lead."— Presentation transcript:
Introduction 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! Data warehousing and data mining: –On-line analytical processing – query-driven data analysis –The efficient discovery of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases
Evolution of Database Technology 1960s: –Data collection, database creation, IMS and network DBMS 1970s: –Relational data model, relational DBMS 1980s: –RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.) 1990s: –Data mining and data warehousing, multimedia databases, and Web technology
What is data mining? Data mining: the process of efficient discovery of previously unknown patterns, relationships, rules in large databases and data warehouses Goal: help the human analyst to understand the data SQL query: –How many bottles of wine did we sell in 1 st Qtr of 1999 in Poland vs Austria?
What is data mining? Data mining query: –How do the buyers of wine in Poland and Austria differ? –What else do the buyers of wine in Austria buy along with wine? –How the buyers of wine can be characterized?
What is data mining? Data mining (knowledge discovery in databases): –Extraction of interesting ( non-trivial, implicit, previously unknown and potentially useful) information from data in large databases Alternative names and their “inside stories”: –Knowledge discovery in databases (KDD: SIGKDD), knowledge extraction, data archeology, data dredging, information harvesting, business intelligence, etc. –Data mining: a misnomer? What is not data mining? –Expert systems or small statistical programs –OLAP
Data Mining: A KDD Process Steps of a KDD Process: –Learning the application domain: relevant prior knowledge and goals of application –Creating a target data set: data selection –Data cleaning and preprocessing: (may take 60% of effort!) –Data reduction and projection: –Find useful features, dimensionality/variable reduction, invariant representation. –Choosing functions of data mining summarization, classification, regression, association, clustering. –Choosing the mining algorithm(s) –Data mining: search for patterns of interest –Interpretation: analysis of results. visualization, transformation, removing redundant patterns, etc. –Use of discovered knowledge
Data Mining and Business Intelligence Increasing potential to support business decisions Data Sources Paper, Files, Database systems, OLTP, WWW Data Warehouses/Data Marts OLAP, MDA Data Exploration Statistical Analysis, Reporting Data Mining Information Discovery Data Presentation Visualization Making Decisions End User DBA Business Analyst Data Analyst
Data Warehouse Meta Data MDDB OLAM Engine OLAP Engine User GUI API Data Cube API Database API Data cleaning Data integration Filtering Databases Filtering&Integration Mining queryMining result An OLAM Architecture
Data Mining: Confluence of Multiple Disciplines Database systems, data warehouse and OLAP Statistics Machine learning Visualization Information science High performance computing Other disciplines: –Neural networks, mathematical modeling, information retrieval, pattern recognition, etc.
Data Mining: On What Kind of Data? Relational databases Data warehouses Transactional databases Advanced DB systems and information repositories –Object-oriented and object-relational databases –Spatial databases –Time-series data and temporal data –Text databases and multimedia databases –Heterogeneous and legacy databases –WWW
Data Mining Functionality Data mining methods may be classified onto 6 basic classes: Associations –Finding rules like “if the customer buys mustard, sausage, and beer, then the probability that he/she buys chips is 50%” Classifications –Classify data based on the values of the decision attribute, e.g. classify patients based on their “state” Clustering –Group data to form new classes, cluster customers based on their behavior to find common patterns
Data Mining Functionality Sequential patterns –Finding rules like “if the customer buys TV, then, few days later, he/she buys camera, then the probability that he/she will buy within 1 month video is 50%” Time-Series similarities –Finding similar sequences (or subsequences) in time- series (e.g. stock analysis) Outlier detection –Finding anomalies/exceptions/deviations in data