scholarly journals Data Mining: Machine Learning and Statistical Techniques

10.5772/13621 ◽  
2011 ◽  
Author(s):  
Alfonso Palmer ◽  
Rafael Jimenez ◽  
Elena Gervill
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mining Machine ◽  
Statistical Techniques

scholarly journals Data Mining, Machine Learning, Deep Learning, Chemometrics. Definitions, common points and Trends (Spoiler Alert: VALIDATE your models!)

2021 ◽  
Vol 8 (32) ◽  
pp. 22-38
Author(s):  
José Manuel Amigo
Keyword(s):  
Analytical Chemistry ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Daily Life ◽  
Big Data Analysis ◽  
Mining Machine ◽  
Learning Data ◽  
Made In

Concepts like Machine Learning, Data Mining or Artificial Intelligence have become part of our daily life. This is mostly due to the incredible advances made in computation (hardware and software), the increasing capabilities of generating and storing all types of data and, especially, the benefits (societal and economical) that generate the analysis of such data. Simultaneously, Chemometrics has played an important role since the late 1970s, analyzing data within natural science (and especially in Analytical Chemistry). Even with the strong parallelisms between all of the abovementioned terms and being popular with most of us, it is still difficult to clearly define or differentiate the meaning of Machine Learning, Data Mining, Artificial Intelligence, Deep Learning and Chemometrics. This manuscript brings some light to the definitions of Machine Learning, Data Mining, Artificial Intelligence and Big Data Analysis, defines their application ranges and seeks an application space within the field of analytical chemistry (a.k.a. Chemometrics). The manuscript is full of personal, sometimes probably subjective, opinions and statements. Therefore, all opinions here are open for constructive discussion with the only purpose of Learning (like the Machines do nowadays).

Data mining/machine learning methods in foodomics

2021 ◽  
Vol 37 ◽  
pp. 76-82
Author(s):  
Ana M Jimenez-Carvelo ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mining Machine ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals A Systematic Review of Deep Learning Approaches to Educational Data Mining

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Antonio Hernández-Blanco ◽  
Boris Herrera-Flores ◽  
David Tomás ◽  
Borja Navarro-Colorado
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Language Processing ◽  
Educational Data Mining ◽  
Research Field ◽  
Machine Learning Techniques ◽  
Mining Machine ◽  
Learning Approaches ◽  
Learning Techniques

Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.

scholarly journals Smart Tour Route Planning Algorithm Based on Naïve Bayes Interest Data Mining Machine Learning

2020 ◽  
Vol 9 (2) ◽  
pp. 112 ◽  
Author(s):  
Xiao Zhou ◽  
Mingzhan Su ◽  
Zhong Liu ◽  
Yu Hu ◽  
Bin Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Weight Function ◽  
Closed Loop ◽  
Naive Bayes ◽  
Route Planning ◽  
Naïve Bayes ◽  
Mining Machine ◽  
Planning Algorithm ◽  
Set Up

A smart tour route planning algorithm based on a Naïve Bayes interest data mining machine learning is brought forward in the paper, according to the problems of current tour route planning methods. A machine learning model of Naïve Bayes interest data mining is set up by learning a mass of training data on tourists’ interests and needs. Through the recommended interest tourist site classifications from the machine learning module, the optimal tourist site mining algorithm based on the membership degree searching propagating tree of a tourist’s temporary accommodation is set up, which mines and outputs the optimal tourist sites. The mined optimal tourist sites are taken as seed points to set up a tour route planning algorithm based on the optimal propagating tree of a closed-loop structure. Through the proposed algorithm, an experiment is designed and performed to output optimal tour routes conforming to tourists’ needs and interests, including the propagating tree closed-loop structures, a minimum heap of propagating tree weight function value, and a weight function value complete binary tree. We prove that the proposed algorithm has the features of intelligence and accuracy, and it can learn tourists’ needs and interests to output optimal tourist sites and tour routes and ensure that tourists can get the best motive benefits and travel experience in the tour process, by analyzing the experiment data and results.

Introduction to The Special Section on Bias and Fairness in AI

2021 ◽  
Vol 23 (1) ◽  
pp. 1-3
Author(s):  
Toon Calders ◽  
Eirini Ntoutsi ◽  
Mykola Pechenizkiy ◽  
Bodo Rosenhahn ◽  
Salvatore Ruggieri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Health Care ◽  
Special Section ◽  
Resource Planning ◽  
Health Care Resource ◽  
Intelligence Community ◽  
Mining Machine ◽  
Planning Decisions

Fairness in Artificial Intelligence rightfully receives a lot of attention these days. Many life-impacting decisions are being partially automated, including health-care resource planning decisions, insurance and credit risk predictions, recidivism predictions, etc. Much of work appearing on this topic within the Data Mining, Machine Learning and Artificial Intelligence community is focused on technological aspects. Nevertheless, fairness is much wider than this as it lies at the intersection of philosophy, ethics, legislation, and practical perspectives. Therefore, to fill this gap and bring together scholars of these disciplines working on fairness, the first workshop on Bias and Fairness in AI was held online on September 18, 2020 at the ECML-PKDD 2020 conference. This special section includes six articles presenting different perspectives on bias and fairness from different angles.

scholarly journals Applying of a Company’s Stock Price Prediction Using Data Mining

2019 ◽  
Vol 8 (2) ◽  
pp. 2847-2850
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Stock Prices ◽  
Stock Price ◽  
Mining Machine ◽  
Support Vector ◽  
Product Reviews ◽  
Stock Price Prediction

Stock market analysis is a common economic activity that has been an attractive topic to research and used in different forms of day-to-day life in order to predict the stock prices. Techniques like major analysis, Statistical investigation, Time arrangement analysis and so on are reliably worthy forecast device. In this paper, Data mining, Machine learning (ML) and Sentiment analysis are techniques used for analyzing public emotions in order predict the future stock prices. The goal of a project is to review totally different techniques to predict stock worth movement victimization the sentiment analysis from social media, data processing. Sentiment classifiers are designed for social media text like product reviews, blog posts, and email corpus messages. In the company’s communication network, information mining calculation is utilized as to mine email correspondence records and verifiable stock costs. Implementing various Machine learning and Classification models such as Deep Neural network, Random forests, Support Vector Machine, the company can successfully implemented a company-specific model capable of predicting stock price movement with efficient accuracy

Der Weg von Descriptive zu Predictive Six Sigma/The path from descriptive to predictive Six Sigma

2019 ◽  
Vol 109 (11-12) ◽  
pp. 807-810
Author(s):  
F. Schäfer ◽  
E. Schwulera ◽  
H. Otten ◽  
J. Franke
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Continuous Improvement ◽  
Six Sigma ◽  
Data Availability ◽  
Mining Machine ◽  
Production Processes ◽  
Open Source Data ◽  
Source Data

Die Entwicklungen im Bereich der Automatisierung hin zu steigender Datendurchgängigkeit in Kombination mit der Verfügbarkeit frei zugänglicher Datenanalyseplattformen   und -algorithmen erlauben neue Ansätze zur kontinuierlichen Verbesserung von Produktionsprozessen. Auch etablierte Vorgehensweisen wie Six Sigma können und müssen in diesem Rahmen weitergedacht und angereichert werden. Folglich gilt es, die klassische, hauptsächlich deskriptive Herangehensweise von Six Sigma um relevante Methoden und Algorithmen aus den Bereichen Data Mining, Machine Learning und künstliche Intelligenz zu erweitern. Die klassische Six Sigma Ausbildung bietet für diesen Wandel gute Voraussetzungen, die es auszubauen und anzupassen gilt.   The increasing data availability in combination with open source data analysis platforms and algorithms pave the way for new ways of operationalizing continuous improvement tasks in the field of production processes. Even established approaches like Six Sigma need to be enhanced and enriched in this context. Consequently, the classical and more descriptive nature of Six Sigma should consider relevant methods and algorithms out of the field of data mining, machine learning and artificial intelligence. The classical Six Sigma training provides a good basis for this change to broaden the Six Sigma scope and its toolbox.

Clustering and Bayesian Networks

2020 ◽  
pp. 50-73
Author(s):  
Bhanu Chander
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Bayesian Network ◽  
Learning Communities ◽  
Mining Machine ◽  
Belief Networks ◽  
Classification Methods ◽  
Probabilistic Information ◽  
Training Stage ◽  
Relationship Of

The goal of this chapter is to present an outline of clustering and Bayesian schemes used in data mining, machine learning communities. Standardized data into sensible groups is the preeminent mode of understanding as well as learning. A cluster constitutes a set regarding entities that are alike and entities from different clusters are not alike. Representing data by fewer clusters inevitably loses certain fine important information but achieves better simplification. There is no training stage in clustering; mostly, it's used when the classes are not well-known. Bayesian network is one of the best classification methods and is frequently used. Generally, Bayesian network is a form of graphical probabilistic representation model that consists of a set of interconnected nodes, where each node represents a variable, and inter-link connection represents a causal relationship of those variables. Belief networks are graph symbolized models that successfully model familiarity via transmitting probabilistic information to a variety of assumptions.

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