- Year not given · 2+6 marks
First two phases of KDD
Short answer
KDD is the overall knowledge discovery process; data mining is just one step. The first two phases are data cleaning (removing noise, filling missing values) and data integration (merging multiple sources). Example: a university cleans marks and integrates exam, library, and fee databases.
Is KDD a data mining? Explain the first two phases of KDD with example.
Is KDD data mining? No. KDD (Knowledge Discovery in Databases) is the whole process of discovering useful knowledge from data. It has several steps: cleaning, integration, selection, transformation, data mining, pattern evaluation and presentation. Data mining is only one of these steps, the one in which intelligent algorithms extract patterns. So data mining is a part of KDD, although the two names are often used as synonyms.
First phase: Data cleaning
Data cleaning removes noise and inconsistent data and handles missing values, because real data is dirty. Techniques: fill missing values (mean, most probable value), smooth noise by binning or regression, remove duplicates and correct inconsistencies.
Second phase: Data integration
Data integration combines data from multiple sources (databases, files, data cubes) into one coherent store. Issues: schema integration, entity identification (cust_id vs customer_no), redundancy (detected by correlation analysis) and conflicting values (different units).
Example: university
A university wants to find the factors behind student failure.
- Cleaning: the marks table has blanks (missing marks are replaced with the subject average) and a mark of 950 (a typo, corrected to 95); duplicate student records are removed.
- Integration: the exam database (
roll_no), library database (student_id) and fee database (reg_no) are combined into one student table by matching these identifiers; marks stored as percentages and as GPA are converted to one unit.
The cleaned, integrated data is then selected, transformed and mined.
✓ Answer checked by Claude Opus 5.5 (high effort)
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