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Data Mining: Concepts & Techniques Data Mining: Concepts & Techniques

Motivation: Necessity is the Mother of Invention • Data explosion problem – Automated data Motivation: Necessity is the Mother of Invention • 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 warehousing and data mining – Data warehousing and on-line analytical processing – Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases

Evolution of Database Technology Evolution of Database Technology

What Is Data Mining? • Data mining (knowledge discovery in databases): – Extraction of 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 archeology, data dredging, information harvesting, business intelligence, etc. • What is not data mining? – (Deductive) query processing. – Expert systems or small ML/statistical programs

Data Mining: A KDD Process Data mining: the core of knowledge discovery process Data Mining: A KDD Process Data mining: the core of knowledge discovery process

Steps of a KDD Process • Learning the application domain: – relevant prior knowledge 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 transformation: – 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 • Pattern evaluation and knowledge presentation – visualization, transformation, removing redundant patterns, etc. • Use of discovered knowledge

Knowledge Discovery Process • The whole process of extraction of implicit, previously unknown and Knowledge Discovery Process • The whole process of extraction of implicit, previously unknown and potentially useful knowledge from a large database – It includes data selection, cleaning, enrichment, coding, data mining, and reporting – Data Mining is the key stage of Knowledge Discovery Process • The process of finding the desired information from large database

Knowledge Discovery Process • Example: the database of a magazine publisher which sells five Knowledge Discovery Process • Example: the database of a magazine publisher which sells five types of magazines – on cars, houses, sports, music and comics – Data mining: • Find interesting categorical properties – Questions: • What is the profile of a reader of a car magazine? • Is there any correlation between an interest in cars and an interest in comics? • The knowledge discovery process consists of six stages

Data Selection • Select the information about people who have subscribed to a magazine Data Selection • Select the information about people who have subscribed to a magazine

Cleaning • Pollutions: Type errors, moving from one place to another without notifying change Cleaning • Pollutions: Type errors, moving from one place to another without notifying change of address, people give incorrect information about themselves – Pattern Recognition Algorithms

Cleaning • Lack of domain consistency Cleaning • Lack of domain consistency

Enrichment • Need extra information about the clients consisting of date of birth, income, Enrichment • Need extra information about the clients consisting of date of birth, income, amount of credit, and whether or not an individual owns a car or a house

Enrichment • The new information need to be easily joined to the existing client Enrichment • The new information need to be easily joined to the existing client records – Extract more knowledge

Coding • We select only those records that have enough information to be of Coding • We select only those records that have enough information to be of value (row) • Project the fields in which we are interested (column)

Coding • Code the information which is too detailed – Address to region – Coding • Code the information which is too detailed – Address to region – Birth date to age – Divide income by 1000 – Divide credit by 1000 – Convert cars yes-no to 1 -0 – Convert purchase date to month numbers starting from 1990 • The way in which we code the information will determine the type of patterns we find • Coding has to be performed repeatedly in order to get the best results

Coding • The way in which we code the information will determine the type Coding • The way in which we code the information will determine the type of patterns we find

Coding • We are interested in the relationships between readers of different magazines – Coding • We are interested in the relationships between readers of different magazines – Perform flattening operation

Data mining • We may find the following rules – A customer with credit Data mining • We may find the following rules – A customer with credit > 13000 and aged between 22 and 31 who has subscribed to a comics at time T will very likely subscribe to a car magazine five years later – The number of house magazines sold to customers with credit between 12000 and 31000 living in region 4 is increasing – A customer with credit between 5000 and 10000 who reads a comics magazine will very likely become a customer with credit between 12000 and 31000 who reads a sports and a house magazine after 12 years

Knowledge Discovery Process Knowledge Discovery Process

Business-Question-Driven Process Business-Question-Driven Process

Data Mining and Business Intelligence Increasing potential to support business decisions Making Decisions Data Data Mining and Business Intelligence Increasing potential to support business decisions Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery End User Business Analyst Data Exploration Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts OLAP, MDA Data Sources Paper, Files, Information Providers, Database Systems, OLTP DBA

Architecture of a Typical Data Mining System Architecture of a Typical Data Mining System

Data Mining: On What Kind of Data? • • Relational databases Data warehouses Transactional Data Mining: On What Kind of Data? • • Relational databases Data warehouses Transactional databases Advanced DB and information repositories – Object-oriented and object-relational databases – Spatial databases – Time-series data and temporal data – Text databases and multimedia databases – Heterogeneous databases – WWW