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Data mining ch1

Summaries of Data mining chapter 1
Course

Data Mining

91 Documents
Students shared 91 documents in this course
Academic year: 2021/2022
Listed bookData Mining: Concepts and Techniques
Uploaded by:
MM
Mahmoud Mohamed
Assiut University
28Uploads
41upvotes

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  1. Data mining (knowledge discovery from data) Extraction of interesting patterns or knowledge from huge amount of data.

  2. interesting patterns → non-trivial, implicit, previously unknown and potentially useful

  3. Alternative names → Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology.

  4. Knowledge Discovery (KDD) Process

  5. Data Mining: Confluence of Multiple Disciplines

  6. Classification Schemes: General functionality

    • Descriptive data mining.
    • Predictive data mining.

  7. Data Mining Functionalities:

    • Multidimensional concept description: Characterization and discrimination
    • Frequent patterns, association, correlation vs. causality
    • Classification and prediction
    • Cluster analysis: Maximizing intra-class similarity & minimizing interclass similarity
    • Outlier analysis: Noise or exception? Useful in fraud detection.
    • Trend and evolution analysis

  8. Top-10 Most Popular DM Algorithms:

    • C4 → Classification
    • K-Means → Clustering
    • SVM → Statistical Learning
    • Apriori → Statistical Learning
    • EM → Statistical Learning
    • PageRank → Link Mining
    • AdaBoost → Bagging and Boosting
    • kNN → Classification
    • Naive Bayes → Classification
    • CART → Classification

  9. Major Issues in Data Mining:

    • Mining methodology
    • User interaction
    • Applications and social impacts
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Data mining ch1

Course: Data Mining

91 Documents
Students shared 91 documents in this course

University: Assiut University

Was this document helpful?
1. Data mining (knowledge discovery from data) Extraction of interesting patterns or
knowledge from huge amount of data.
2. interesting patterns → non-trivial, implicit, previously unknown and potentially useful
3. Alternative names → Knowledge discovery (mining) in databases (KDD), knowledge
extraction, data/pattern analysis, data archeology.
4. Knowledge Discovery (KDD) Process
5. Data Mining: Confluence of Multiple Disciplines
6. Classification Schemes: General functionality
• Descriptive data mining.
• Predictive data mining.
7. Data Mining Functionalities:
• Multidimensional concept description: Characterization and discrimination
• Frequent patterns, association, correlation vs. causality
• Classification and prediction
• Cluster analysis: Maximizing intra-class similarity & minimizing interclass similarity
• Outlier analysis: Noise or exception? Useful in fraud detection.
• Trend and evolution analysis

Students also viewed

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