# Clustering high dimensional data using RIA

## Abstract

Clustering may simply represent a convenient method for organizing a large data set so that it can easily be understood and information can efficiently be retrieved. However, identifying cluster in high dimensionality data sets is a difficult task because of the curse of dimensionality. Another challenge in clustering is some traditional functions cannot capture the pattern dissimilarity among objects. In this article, we used an alternative dissimilarity measurement called Robust Influence Angle (RIA) in the partitioning method. RIA is developed using eigenstructure of the covariance matrix and robust principal component score. We notice that, it can obtain cluster easily and hence avoid the curse of dimensionality. It is also manage to cluster large data sets with mixed numeric and categorical value.

- Authors:

- School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah (Malaysia)

- Publication Date:

- OSTI Identifier:
- 22391657

- Resource Type:
- Journal Article

- Journal Name:
- AIP Conference Proceedings

- Additional Journal Information:
- Journal Volume: 1660; Journal Issue: 1; Conference: ICoMEIA 2014: International Conference on Mathematics, Engineering and Industrial Applications 2014, Penang (Malaysia), 28-30 May 2014; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-243X

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; DATA PROCESSING; EIGENVALUES; FUNCTIONS; MATHEMATICAL MODELS; MATHEMATICAL SOLUTIONS; MATRICES; PARTITION

### Citation Formats

```
Aziz, Nazrina.
```*Clustering high dimensional data using RIA*. United States: N. p., 2015.
Web. doi:10.1063/1.4915706.

```
Aziz, Nazrina.
```*Clustering high dimensional data using RIA*. United States. doi:10.1063/1.4915706.

```
Aziz, Nazrina. Fri .
"Clustering high dimensional data using RIA". United States. doi:10.1063/1.4915706.
```

```
@article{osti_22391657,
```

title = {Clustering high dimensional data using RIA},

author = {Aziz, Nazrina},

abstractNote = {Clustering may simply represent a convenient method for organizing a large data set so that it can easily be understood and information can efficiently be retrieved. However, identifying cluster in high dimensionality data sets is a difficult task because of the curse of dimensionality. Another challenge in clustering is some traditional functions cannot capture the pattern dissimilarity among objects. In this article, we used an alternative dissimilarity measurement called Robust Influence Angle (RIA) in the partitioning method. RIA is developed using eigenstructure of the covariance matrix and robust principal component score. We notice that, it can obtain cluster easily and hence avoid the curse of dimensionality. It is also manage to cluster large data sets with mixed numeric and categorical value.},

doi = {10.1063/1.4915706},

journal = {AIP Conference Proceedings},

issn = {0094-243X},

number = 1,

volume = 1660,

place = {United States},

year = {2015},

month = {5}

}