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DOI: 10.14569/IJACSA.2017.080750
PDF

Efficient Feature Selection for Product Labeling over Unstructured Data

Author 1: Zeki YETGIN
Author 2: Abdullah ELEWI
Author 3: Furkan GÖZÜKARA

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 7, 2017.

  • Abstract and Keywords
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Abstract: The paper introduces a novel feature selection algorithm for labeling identical products collected from online web resources. Product labeling is important for clustering similar or same products. Products blindly crawled over the web sources, such as online sellers, have unstructured data due to having features expressed in different representations and formats. Such data result in feature vectors whose representation is unknown and non-uniform in length. Thus, product labeling, as a challenging problem, needs efficient selection of features that best describe the products. In this paper, an efficient feature selection algorithm is proposed for product labeling problem. Hierarchical clustering is used with the state of the art similarity metrics to assess the performance of the proposed algorithm. The results show that the proposed algorithm increases the performance of product labeling significantly. Furthermore, the method can be applied to any clustering algorithm that works on unstructured data.

Keywords: Product labeling; product clustering; feature selection; similarity metrics; hierarchical clustering

Zeki YETGIN, Abdullah ELEWI and Furkan GÖZÜKARA, “Efficient Feature Selection for Product Labeling over Unstructured Data” International Journal of Advanced Computer Science and Applications(IJACSA), 8(7), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080750

@article{YETGIN2017,
title = {Efficient Feature Selection for Product Labeling over Unstructured Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080750},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080750},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {7},
author = {Zeki YETGIN and Abdullah ELEWI and Furkan GÖZÜKARA}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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