Data Mining Lectures for beginners, Lecture notes of Data Mining

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Data Mining

Slides prepared by Irzam Sarfraz

Book

• DATA MINING

Concepts, Models, Methods, and Algorithms

• (^) Author: Mehmed Kantardzic
• (^) PDF available at: http://sites.google.com/view/dmgmdc

Data Mining

• (^) Discovering and utilizing patterns in large datasets

Objectives of Data Mining

• (^) Predictive Data Mining
• (^) Descriptive Data Mining

Data Mining Process

• (^) State the problem and formulate the hypothesis
• (^) Collect the data
• (^) Preprocess the data
• (^) Scaling, encoding, and selecting features
• (^) Estimate the model
• (^) Interpret the model and draw conclusions

Large Datasets

• (^) Problem?
• (^) Size and dimensionality are too large for manual analysis
• (^) Not even possible with semi-automatic computer methods e.g. a calculator
• (^) But conclusions from large datasets can be very effective and promising

Data in Data Mining

• (^) Structured e.g. business transactions in databases
• (^) Semi-Structured e.g. webpages
• (^) Unstructured e.g. videos

Unobserved Variables

• (^) Variables that influence the system but are not available or recorded
• (^) Maybe due to high cost/complexity or not understanding the need
• (^) Leads to inaccuracies

Data Warehouses

• (^) An organizations repository of data storing millions or billions of records for strategic decision making
• (^) Respond to queries from decision makers
• (^) Problem: May produce misleading results if data is not standardized or integrated
• (^) Solution: Use transformations to accurately structure data

Reading Assignment

• (^) Article 1.
• (^) Why a data mining project fails?
• (^) Due next week first lecture