Supervised and Unsupervised Learning for Data Science

2020 ◽  
Keyword(s):  
Unsupervised Learning ◽  
Data Science ◽  
Supervised And Unsupervised Learning

scholarly journals Clustering in Wineinformatics with Attribute Selection to Increase Uniqueness of Clusters

Fermentation ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 27
Author(s):  
Jared McCune ◽  
Alex Riley ◽  
Bernard Chen
Keyword(s):  
Unsupervised Learning ◽  
Data Science ◽  
Science Research ◽  
Research Area ◽  
Attribute Selection ◽  
Short List ◽  
Wine Quality ◽  
Filtration Process ◽  
Target Region ◽  
Related Data

Wineinformatics is a new data science research area that focuses on large amounts of wine-related data. Most of the current Wineinformatics researches are focused on supervised learning to predict the wine quality, price, region and weather. In this research, unsupervised learning using K-means clustering with optimal K search and filtration process is studied on a Bordeaux-region specific dataset to form clusters and find representative wines in each cluster. 14,349 wines representing the 21st century Bordeaux dataset are clustered into 43 and 13 clusters with detailed analysis on the number of wines, dominant wine characteristics, average wine grades, and representative wines in each cluster. Similar research results are also generated and presented on 435 elite wines (wines that scored 95 points and above on a 100 points scale). The information generated from this research can be beneficial to wine vendors to make a selection given the limited number of wines they can realistically offer, to connoisseurs to study wines in a target region/vintage/price with a representative short list, and to wine consumers to get recommendations. Many possible researches can adopt the same process to analyze and find representative wines in different wine making regions/countries, vintages, or pivot points. This paper opens up a new door for Wineinformatics in unsupervised learning researches.

scholarly journals Feature Selection Optimization for Highlighting Opinions Using Supervised and Unsupervised Learning on Arabic Language

2021 ◽  
Vol 10 (2) ◽  
pp. 636-642
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Text Mining ◽  
Unsupervised Learning ◽  
Arabic Language ◽  
Machine Learning Techniques ◽  
Business Decision ◽  
Supervised And Unsupervised Learning ◽  
Proposed Model ◽  
The Impact

Text mining utilizes machine learning (ML) and natural language processing (NLP) for text implicit knowledge recognition, such knowledge serves many domains as translation, media searching, and business decision making. Opinion mining (OM) is one of the promised text mining fields, which are used for polarity discovering via text and has terminus benefits for business. ML techniques are divided into two approaches: supervised and unsupervised learning, since we herein testified an OM feature selection(FS)using four ML techniques. In this paper, we had implemented number of experiments via four machine learning techniques on the same three Arabic language corpora. This paper aims at increasing the accuracy of opinion highlighting on Arabic language, by using enhanced feature selection approaches. FS proposed model is adopted for enhancing opinion highlighting purpose. The experimental results show the outperformance of the proposed approaches in variant levels of supervisory,i.e. different techniques via distinct data domains. Multiple levels of comparison are carried out and discussed for further understanding of the impact of proposed model on several ML techniques.

Fusion of supervised and unsupervised learning for improved classification of hyperspectral images

2012 ◽  
Vol 217 ◽  
pp. 39-55 ◽  
Author(s):  
Naif Alajlan ◽  
Yakoub Bazi ◽  
Farid Melgani ◽  
Ronald R. Yager
Keyword(s):  
Unsupervised Learning ◽  
Hyperspectral Images ◽  
Supervised And Unsupervised Learning
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