Search query clustering comparation on e-commerce using k-Means and adaptive DBSCAN

null Darwin; Ronsen Purba; Muhammad Fermi Pasha

doi:10.1109/MECnIT48290.2020.9166610

Search query clustering comparation on e-commerce using k-Means and adaptive DBSCAN

Darwin, Ronsen Purba, Muhammad Fermi Pasha

Malaysia Sch of Information Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

2 Citations (Scopus)

Abstract

Search queries on electronic commerce vary greatly depending on user behavior and preference. They are funneled through queries that imply buying interest. The accuracy of the search query clustering comparison can be enhanced through either k-Means or adaptive DBSCAN which had not been conducted by previous studies. Clustering helps to derive business knowledge models, especially taxonomic search features. This study used dataset which consisted of 2.074 records as a result of pre-processing, the accuracy was obtained 92.63% using adaptive DBSCAN and 91.75% using k-Means. The taxonomic results help to produce more informative query outputs that can be used to improve the appearance of electronic commerce and search features. Thus, increase of customers satisfaction and conversions can be achieved.

Original language	English
Title of host publication	MECnIT 2020 - International Conference on Mechanical, Electronics, Computer, and Industrial Technology
Editors	Riski Titian Ginting
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	207-211
Number of pages	5
ISBN (Electronic)	9781728174037
ISBN (Print)	9781728174044
DOIs	https://doi.org/10.1109/MECnIT48290.2020.9166610
Publication status	Published - 2020
Event	International Conference on Mechanical, Electronics, Computer, and Industrial Technology 2020 - Medan, Indonesia Duration: 25 Jun 2020 → 26 Jun 2020 Conference number: 3rd https://ieeexplore.ieee.org/xpl/conhome/9163792/proceeding (Proceedings)

Conference

Conference	International Conference on Mechanical, Electronics, Computer, and Industrial Technology 2020
Abbreviated title	MECnIT 2020
Country/Territory	Indonesia
City	Medan
Period	25/06/20 → 26/06/20
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9163792/proceeding (Proceedings)

Keywords

Adaptive DBSCAN
K-Means
Search Behavior
Search Query Clustering
Taxonomy

Access to Document

10.1109/MECnIT48290.2020.9166610

Cite this

Darwin, Purba, R., & Pasha, M. F. (2020). Search query clustering comparation on e-commerce using k-Means and adaptive DBSCAN. In R. Titian Ginting (Ed.), MECnIT 2020 - International Conference on Mechanical, Electronics, Computer, and Industrial Technology (pp. 207-211). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/MECnIT48290.2020.9166610

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title = "Search query clustering comparation on e-commerce using k-Means and adaptive DBSCAN",

abstract = "Search queries on electronic commerce vary greatly depending on user behavior and preference. They are funneled through queries that imply buying interest. The accuracy of the search query clustering comparison can be enhanced through either k-Means or adaptive DBSCAN which had not been conducted by previous studies. Clustering helps to derive business knowledge models, especially taxonomic search features. This study used dataset which consisted of 2.074 records as a result of pre-processing, the accuracy was obtained 92.63% using adaptive DBSCAN and 91.75% using k-Means. The taxonomic results help to produce more informative query outputs that can be used to improve the appearance of electronic commerce and search features. Thus, increase of customers satisfaction and conversions can be achieved. ",

keywords = "Adaptive DBSCAN, K-Means, Search Behavior, Search Query Clustering, Taxonomy",

author = "Darwin and Ronsen Purba and Pasha, {Muhammad Fermi}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; International Conference on Mechanical, Electronics, Computer, and Industrial Technology 2020, MECnIT 2020 ; Conference date: 25-06-2020 Through 26-06-2020",

year = "2020",

doi = "10.1109/MECnIT48290.2020.9166610",

language = "English",

isbn = "9781728174044",

pages = "207--211",

editor = "{Titian Ginting}, Riski",

booktitle = "MECnIT 2020 - International Conference on Mechanical, Electronics, Computer, and Industrial Technology",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/9163792/proceeding",

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Darwin, Purba, R & Pasha, MF 2020, Search query clustering comparation on e-commerce using k-Means and adaptive DBSCAN. in R Titian Ginting (ed.), MECnIT 2020 - International Conference on Mechanical, Electronics, Computer, and Industrial Technology. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 207-211, International Conference on Mechanical, Electronics, Computer, and Industrial Technology 2020, Medan, Indonesia, 25/06/20. https://doi.org/10.1109/MECnIT48290.2020.9166610

Search query clustering comparation on e-commerce using k-Means and adaptive DBSCAN. / Darwin; Purba, Ronsen; Pasha, Muhammad Fermi.
MECnIT 2020 - International Conference on Mechanical, Electronics, Computer, and Industrial Technology. ed. / Riski Titian Ginting. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2020. p. 207-211.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

TY - GEN

T1 - Search query clustering comparation on e-commerce using k-Means and adaptive DBSCAN

AU - Darwin, null

AU - Purba, Ronsen

AU - Pasha, Muhammad Fermi

N1 - Conference code: 3rd

PY - 2020

Y1 - 2020

N2 - Search queries on electronic commerce vary greatly depending on user behavior and preference. They are funneled through queries that imply buying interest. The accuracy of the search query clustering comparison can be enhanced through either k-Means or adaptive DBSCAN which had not been conducted by previous studies. Clustering helps to derive business knowledge models, especially taxonomic search features. This study used dataset which consisted of 2.074 records as a result of pre-processing, the accuracy was obtained 92.63% using adaptive DBSCAN and 91.75% using k-Means. The taxonomic results help to produce more informative query outputs that can be used to improve the appearance of electronic commerce and search features. Thus, increase of customers satisfaction and conversions can be achieved.

AB - Search queries on electronic commerce vary greatly depending on user behavior and preference. They are funneled through queries that imply buying interest. The accuracy of the search query clustering comparison can be enhanced through either k-Means or adaptive DBSCAN which had not been conducted by previous studies. Clustering helps to derive business knowledge models, especially taxonomic search features. This study used dataset which consisted of 2.074 records as a result of pre-processing, the accuracy was obtained 92.63% using adaptive DBSCAN and 91.75% using k-Means. The taxonomic results help to produce more informative query outputs that can be used to improve the appearance of electronic commerce and search features. Thus, increase of customers satisfaction and conversions can be achieved.

KW - Adaptive DBSCAN

KW - K-Means

KW - Search Behavior

KW - Search Query Clustering

KW - Taxonomy

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U2 - 10.1109/MECnIT48290.2020.9166610

DO - 10.1109/MECnIT48290.2020.9166610

M3 - Conference Paper

AN - SCOPUS:85091532951

SN - 9781728174044

SP - 207

EP - 211

BT - MECnIT 2020 - International Conference on Mechanical, Electronics, Computer, and Industrial Technology

A2 - Titian Ginting, Riski

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - International Conference on Mechanical, Electronics, Computer, and Industrial Technology 2020

Y2 - 25 June 2020 through 26 June 2020

ER -

Darwin, Purba R, Pasha MF. Search query clustering comparation on e-commerce using k-Means and adaptive DBSCAN. In Titian Ginting R, editor, MECnIT 2020 - International Conference on Mechanical, Electronics, Computer, and Industrial Technology. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2020. p. 207-211 doi: 10.1109/MECnIT48290.2020.9166610

Search query clustering comparation on e-commerce using k-Means and adaptive DBSCAN

Abstract

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Keywords

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