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Reseach Article

A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and Maintenance

by Memoona Khanum, Tahira Mahboob, Warda Imtiaz, Humaraia Abdul Ghafoor, Rabeea Sehar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 13
Year of Publication: 2015
Authors: Memoona Khanum, Tahira Mahboob, Warda Imtiaz, Humaraia Abdul Ghafoor, Rabeea Sehar
10.5120/21131-4058

Memoona Khanum, Tahira Mahboob, Warda Imtiaz, Humaraia Abdul Ghafoor, Rabeea Sehar . A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and Maintenance. International Journal of Computer Applications. 119, 13 ( June 2015), 34-39. DOI=10.5120/21131-4058

@article{ 10.5120/21131-4058,
author = { Memoona Khanum, Tahira Mahboob, Warda Imtiaz, Humaraia Abdul Ghafoor, Rabeea Sehar },
title = { A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and Maintenance },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 13 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number13/21131-4058/ },
doi = { 10.5120/21131-4058 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:03:58.638268+05:30
%A Memoona Khanum
%A Tahira Mahboob
%A Warda Imtiaz
%A Humaraia Abdul Ghafoor
%A Rabeea Sehar
%T A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and Maintenance
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 13
%P 34-39
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper is comprehensive survey of methodologies and techniques used for Unsupervised Machine Learning that are used for learn complex, highly non-linear models with millions parameters to used large amount of unlabeled data. Deep belief networks (DBNs) and sparse coding are the two well known techniques of unsupervised learning models. Data clustering distinguishes by the absence of category information. Basically structure in data is finding in clustering and it has long history in scientific field . K-means is the most popular and simple clustering algorithm. This Algorithm was published in 1955. Hierarchical matching pursuit (HMP) for RGB-D data is discussed. Sparse coding learns hierarchical feature representations from raw RGB-D data in an unsupervised way by using hierarchical matching pursuit. The formal study of learning systems is deduced from Machine learning; which is a field of research. It has found to be highly interdisciplinary field which acquires and constructs upon ideas from statistics, computer science (engineering), optimization theory, and numerous other disciplines of science and mathematics.

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Index Terms

Computer Science
Information Sciences

Keywords

Clustering Feature Selection Unsupervised Learning Expectation-Maximization