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Data Mining - Outlier Analysis, Study notes of Data Mining

Moradabad Institute of TechnologyData Mining

In this document topics covered which are Outlier Discovery: Deviation-Based Approach, Density-Based Local Outlier Detection, Distance-Based Approach, Statistical Approaches, What Is Outlier Discovery?, Applications.

Typology: Study notes

2010/2011

Uploaded on 09/04/2011

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November 26, 2014 Data Mining: Concepts and
Techniques 1
Chapter 7. Cluster
Analysis
1. What is Cluster Analysis?
2. Types of Data in Cluster Analysis
3. A Categorization of Major Clustering Methods
4. Partitioning Methods
5. Hierarchical Methods
6. Density-Based Methods
7. Grid-Based Methods
8. Model-Based Methods
9. Clustering High-Dimensional Data
10.Constraint-Based Clustering
11.Outlier Analysis
12.Summary
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November 26, 2014 Data Mining: Concepts and 1

Chapter 7. Cluster

Analysis

  1. What is Cluster Analysis?
  2. Types of Data in Cluster Analysis
  3. A Categorization of Major Clustering Methods
  4. Partitioning Methods
  5. Hierarchical Methods
  6. Density-Based Methods
  7. Grid-Based Methods
  8. Model-Based Methods
  9. Clustering High-Dimensional Data 10.Constraint-Based Clustering 11.Outlier Analysis 12.Summary

November 26, 2014 Data Mining: Concepts and 2

What Is Outlier Discovery?

  • (^) What are outliers?
    • (^) The set of objects are considerably dissimilar from the remainder of the data
    • (^) Example: Sports: Michael Jordon, Wayne Gretzky, ...
  • (^) Problem: Define and find outliers in large data sets
  • (^) Applications:
    • (^) Credit card fraud detection
    • (^) Telecom fraud detection
    • (^) Customer segmentation
    • (^) Medical analysis

November 26, 2014 Data Mining: Concepts and 4 Outlier Discovery: Distance-Based Approach

  • (^) Introduced to counter the main limitations imposed by statistical methods - (^) We need multi-dimensional analysis without knowing data distribution
  • (^) Distance-based outlier: A DB(p, D)-outlier is an object O in a dataset T such that at least a fraction p of the objects in T lies at a distance greater than D from O
  • (^) Algorithms for mining distance-based outliers
    • (^) Index-based algorithm
    • (^) Nested-loop algorithm
    • (^) Cell-based algorithm

November 26, 2014 Data Mining: Concepts and 5

Density-Based Local

Outlier Detection

  • (^) Distance-based outlier detection is based on global distance distribution
  • (^) It encounters difficulties to identify outliers if data is not uniformly distributed
  • Ex. C 1 contains 400 loosely distributed points, C 2 has 100 tightly condensed points, 2 outlier points o 1 , o 2
  • (^) Distance-based method cannot identify o 2 as an outlier
  • (^) Need the concept of local outlier  (^) Local outlier factor (LOF)  (^) Assume outlier is not crisp  (^) Each point has a LOF