Search and classification of web shops

Aron Birsa (2017) Search and classification of web shops. EngD thesis.

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Abstract

The aim of the thesis was to develop a tool for automatic classification of online stores depending on the type of products they offer. Websites are classified into seven predefined categories: antiques and collectibles, cloth- ing, consumer electronics, furniture, home and garden, jewelry and office products. The main problem was getting relevant data to build a learning and test data set and classifying web sites. The following machine learning methods were used: naive Bayesian classifier, k-nearest neighbors algorithm, random forests, neural networks and support vector machine. The most promising result were obtained using the support vector machine classifier.

Item Type:

Thesis (EngD thesis)

Keywords:

specialized search engine, data mining, machine learning, e- commerce, text analysis, naive Bayesian classifier, k-nearest neighbors algo- rithm, random forests, neural networks, support vector machine.

Number of Pages:

Language of Content:

Slovenian

Mentor / Comentors:

Name and Surname	ID	Function
izr. prof. dr. Marko Robnik Šikonja	276	Mentor

Link to COBISS:

http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537344451)

Institution:

University of Ljubljana

Department:

Faculty of Computer and Information Science

Item ID:

3757

Date Deposited:

20 Jan 2017 13:28

Last Modified:

02 Feb 2017 10:27

URI:

http://eprints.fri.uni-lj.si/id/eprint/3757

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