Outlier Detection in Location Based Systems By Using Fuzzy Clustering

DOI

10.2991/eusflat-19.2019.91How to use a DOI?

Keywords

Location Based Systems fuzzy clustering segmentation Fuzzy c-Means outlier detection

Abstract

Customer segmentation has been one of most important decision in marketing. In general, demographics of customers, monetary value of customer transactions, types of product/service used are the sources of segmentation process. In recent years, new technology enabled new sources of data. On of these new data are the customer location data collected from location based systems (LBS). By using these location data an improved customer insight can be provided to the companies. Segmentation is an important tool for creating customer insight but anomalies in LBS data can prevent a well formed segmentation. In this paper we propose a novel approach to outlier detection in LBS data by using fuzzy c-means algorithm

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Basar Oztaysi
AU  - Sezi Cevik Onar
AU  - Cengiz Kahraman
PY  - 2019/08
DA  - 2019/08
TI  - Outlier Detection in Location Based Systems By Using Fuzzy Clustering
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 653
EP  - 659
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.91
DO  - 10.2991/eusflat-19.2019.91
ID  - Oztaysi2019/08
ER  -