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# Outlier Analysis

The set of objects are considerably dissimilar from the remainder of the data o Example: Sports: Michael Jordon, Wayne Gretzky

OUTLIER ANALYSIS

The set of objects are considerably dissimilar from the remainder of the data o 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

Statistical Distribution-based outlier detection-Indentify the outlier with respect to the model using discordancy test

How discordancy test work

Data is assumed to be part of a working hypothesis (working hypothesis)-H

Each data object in the dataset is compared to the working hypothesis and is either accepted in the working hypothesis or rejected as discordant into an alternative hypothesis (outliers)- H

Distance-Based outlier detection

Imposed by statistical methods

We need multi-dimensional analysis without knowing data distribution Algorithms for mining distance-based outliers

Index-based algorithm

Indexing Structures such as R-tree (R+-tree), K-D (K-D-B) tree are built for the multi-dimensional database

The index is used to search for neighbors of each object O within radius D around that object.

Once K (K = N(1-p)) neighbors of object O are found, O is not an outlier.

Worst-case computation complexity is O(K*n2), K is the dimensionality and n is the number of objects in the dataset.

Pros: scale well with K

Cons: the index construction process may cost much time

Nested-loop algorithm

Divides the buffer space into two halves (first and second arrays)

Break data into blocks and then feed two blocks into the arrays.

Directly computes the distance between each pair of objects, inside the array or between arrays

Decide the outlier.

Here comes an example:…

Same computational complexity as the index-based algorithm

Pros: Avoid index structure construction

Try to minimize the I/Os n cell based algorithm

Divide the dataset into cells with length

K is the dimensionality, D is the distance

Define Layer-1 neighbors – all the intermediate neighbor cells. The maximum distance between a cell and its neighbor cells is D

Define Layer-2 neighbors – the cells within 3 cell of a certain cell. The minimum distance between a cell and the cells outside of Layer-2 neighbors is D

Criteria

·         Search a cell internally. If there are M objects inside, all the objects in this cell are not outlier

·         Search its layer-1 neighbors. If there are M objects inside a cell and its layer-1 neighbors, all the objects in this cell are not outlier

·         Search its layer-2 neighbors. If there are less than M objects inside a cell, its layer-1 neighbor cells, and its layer-2 neighbor cells, all the objects in this cell are outlier

·         Otherwise, the objects in this cell could be outlier, and then need to calculate the distance between the objects in this cell and the objects in the cells in the layer-2 neighbor cells to see whether the total points within D distance is more than M or not.

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. C1 contains 400 loosely distributed points, C2 has 100 tightly condensed points, 2 outlier points o1, o2

Some outliers can be defined as global outliers, some can be defined as local outliers to a given cluster

O2 would not normally be considered an outlier with regular distance-based outlier detection, since it looks at the global picture

Each data object is assigned a local outlier factor (LOF)

Objects which are closer to dense clusters receive a higher LOF

LOF varies according to the parameter MinPts

Deviation-Based Outlier detection

Identifies outliers by examining the main characteristics of objects in a group

Objects that ―deviate‖ from this description are considered outliers

Sequential exception technique

simulates the way in which humans can distinguish unusual objects from among a series of

supposedly like objects

Dissimilarities  are  assed  between  subsets  in               the  sequence               the  techniques  introduce                the

following key terms

Exception set, dissimilarity function, cardinality function, smoothing factor

OLAP data cube technique

Deviation detection process is overlapped with cube computation

Recomputed measures indicating data exceptions are needed

A cell value is considered an exception if it is significantly different from the expected value, based on a statistical model

Use visual cues such as background color to reflect the degree of exception

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Data Warehousing and Data Mining : Clustering and Applications and Trends in Data Mining : Outlier Analysis |

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