Type: Package
Title: Outlier Detection Using Statistical and Machine Learning Methods
Version: 0.1
Date: 2017-06-06
Author: Siddharth Jain and Prabhanjan Tattar
Maintainer: Siddharth Jain <siddharthjain242@gmail.com>
Description: Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.
License: GPL-2
NeedsCompilation: no
Packaged: 2017-02-06 13:43:28 UTC; siddharthjain
Repository: CRAN
Date/Publication: 2017-02-06 17:38:16

An R package for identifying outliers using statistical and machine learning methods

Description

We intend to provide host of methods for identifying outliers. This will cut across statistical and machine learning methods.

References

M.M. Breunig, H.P. Kriegel, R.T. Ng, and J. Sander. Lof: Identifying density-based local outliers. In Proc. SIGMOD Conf., pages 93-104, 2000.

Examples

data(stiff)
summary(stiff)

Local Correlation Integral

Description

We provide an R implementation of the Local Correlation Integral method for detecting outliers as developed by Breunig, et al. (2000), and we follow its description given in Papadimitriou, et al. (2002).

Usage

LOCI(data, alpha)

Arguments

data

Any R data.frame which consists of numeric values only

alpha

a number in the unit interval for the fractional circle search

Details

A simple implementation is provided here. The core function is the distance function. For each observation, a search is made for nearest neighbors within r distance of it, and then for each of these neighbors, we find the number of observations in the fractional circle. Calculations based on multi-granularity deviation factor, MDEF, help in determining the outlier.

Author(s)

Siddharth Jain and Prabhanjan Tattar

References

M.M. Breunig, H.P. Kriegel, R.T. Ng, and J. Sander. Lof: Identifying density-based local outliers. In Proc. SIGMOD Conf., pages 93-104, 2000. Papadimitriou, S., Kitagawa, H., Gibbons, P.B. and Faloutsos, C., 2003, March. Loci: Fast outlier detection using the local correlation integral. In Data Engineering, 2003. Proceedings. 19th International Conference on (pp. 315-326). IEEE.

Examples

data(stiff)
OM <- LOCI(stiff,0.5)
OM

The Board Stiffness Dataset

Description

Four measures of stiffness of 30 boards are available. The first measure of stiffness is obtained by sending a shock wave down the board, the second measure is obtained by vibrating the board, and remaining are obtained from static tests.

Usage

data(stiff)

Format

A data frame with 30 observations on the following 4 variables.

x1

first measure of stiffness is obtained by sending a shock wave down the board

x2

second measure is obtained by vibrating the board

x3

third measure is obtained by a static test

x4

fourth measure is obtained by a static test

References

Johnson, R.A., and Wichern, D.W. (1982-2007). Applied Multivariate Statistical Analysis, 6e. Pearson Education. Tattar, et al. (2016). A Course in Statistics with R. J. Wiley.

Examples

data(stiff)
summary(stiff)