Data Mining Techniques
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The document provides an overview of data mining, defining its objectives and comparing it to traditional database processing. It outlines basic tasks in data mining such as classification, regression, and clustering, along with examples of data mining queries. The aim is to uncover hidden information from vast amounts of data to meet user expectations for sophisticated information.
Data Mining Techniques
- 1. What is Data Mining E2MATRIX Research Lab Complete Thesis & IEEE Project Help E2matrix 1 E2matrix Opp Phagaara Bus Stand, Parmar Complex, Backside Axis Bank. Phagwara, Punjab, Call : +91 9041262727
- 2. E2matrix 2 Introduction Outline • Define data mining • Data mining vs. databases • Basic data mining tasks • Data mining development • Data mining issues Goal: Provide an overview of data mining.
- 3. E2matrix 3 Introduction • Data is produced at a phenomenal rate • Our ability to store has grown • Users expect more sophisticated information • How? UNCOVER HIDDEN INFORMATION DATA MINING
- 4. E2matrix 4 Data Mining • Objective: Fit data to a model • Potential Result: Higher-level meta information that may not be obvious when looking at raw data • Similar terms – Exploratory data analysis – Data driven discovery – Deductive learning
- 5. E2matrix 5 Data Mining Algorithm • Objective: Fit Data to a Model – Descriptive – Predictive • Preferential Questions – Which technique to choose? • ARM/Classification/Clustering • Answer: Depends on what you want to do with data? – Search Strategy – Technique to search the data • Interface? Query Language? • Efficiency
- 6. E2matrix 6 Database Processing vs. Data Mining Processing • Query – Well defined – SQL • Query – Poorly defined – No precise query language Output – Precise – Subset of database Output – Fuzzy – Not a subset of database
- 7. E2matrix 7 Query Examples • Database • Data Mining – Find all customers who have purchased milk – Find all items which are frequently purchased with milk. (association rules) – Find all credit applicants with last name of Smith. – Identify customers who have purchased more than $10,000 in the last month. – Find all credit applicants who are poor credit risks. (classification) – Identify customers with similar buying habits. (Clustering)
- 8. E2matrix 8 Data Mining Models and Tasks
- 9. E2matrix 9 Basic Data Mining Tasks • Classification maps data into predefined groups or classes – Supervised learning – Pattern recognition – Prediction • Regression is used to map a data item to a real valued prediction variable. • Clustering groups similar data together into clusters. – Unsupervised learning – Segmentation – Partitioning