data warehousing and data mining
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Data mining involves analyzing large amounts of data to uncover hidden patterns and relationships. It aims to fit data to models in order to derive higher-level meta information that may not be obvious from examining the raw data alone. Some basic data mining tasks include classification, which maps data to predefined groups; regression, which maps data to prediction variables; and clustering, which groups similar data together. The goal of data mining is to discover useful information from data.
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data warehousing and data mining
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data warehousing and data mining
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Our ability to store has grown
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Objective: Fit data to a model
Potential Result: Higher-level meta information that may not be obvious when looking at raw data
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- 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