scholarly journals Search for Appropriate Textual Information Sources

2020 ◽  
Author(s):  
Adam Albert ◽  
Marie Duží ◽  
Marek Menšík ◽  
Miroslav Pajr ◽  
Vojtěch Patschka
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Information Retrieval ◽  
Association Rules ◽  
Information Sources ◽  
Intensional Logic ◽  
Textual Information ◽  
Transparent Intensional Logic ◽  
Background Theory ◽  
Mining Methods

In this paper, we deal with the support in the search for appropriate textual sources. Users ask for an atomic concept that is explicated using machine learning methods applied to different textual sources. Next, we deal with the so-obtained explications to provide even more useful information. To this end, we apply the method of computing association rules. The method is one of the data-mining methods used for information retrieval. Our background theory is the system of Transparent Intensional Logic (TIL); all the concepts are formalised as TIL constructions.

Using Association Rules for Query Reformulation

Data Mining ◽  
2013 ◽  
pp. 503-514
Author(s):  
Ismaïl Biskri ◽  
Louis Rompré
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Association Rules ◽  
Text Classification ◽  
Large Volume ◽  
Query Reformulation ◽  
Long Time ◽  
Hidden Knowledge

In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.

Big Data and Service Science

2016 ◽  
pp. 180-196
Author(s):  
Tu-Bao Ho ◽  
Siriwon Taewijit ◽  
Quang-Bach Ho ◽  
Hieu-Chi Dam
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Service Science ◽  
Customer Relationship ◽  
Service Value ◽  
Mining Methods ◽  
The Relationship

Big data is about handling huge and/or complex datasets that conventional technologies cannot handle or handle well. Big data is currently receiving tremendous attention from both industry and academia as there is much more data around us than ever before. This chapter addresses the relationship between big data and service science, especially how big data can contribute to the process of co-creation of service value. In particular, the value co-creation in terms of customer relationship management is mentioned. The chapter starts with brief descriptions of big data, machine learning and data mining methods, service science and its model of value co-creation, and then addresses the key idea of how big data can contribute to co-create service value.

scholarly journals Ensemble Data Mining Methods

2011 ◽  
pp. 448-453
Author(s):  
Nikunj C. Oza
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Prediction Accuracy ◽  
Ensemble Methods ◽  
Multiple Models ◽  
Machine Learning Methods ◽  
Ensemble Data ◽  
Basic Goal ◽  
Mining Methods ◽  
The Individual

Ensemble data mining methods, also known as committee methods or model combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: Each member of the committee should be as competent as possible, but the members should complement one another. If the members are not complementary, that is, if they always agree, then the committee is unnecessary — any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

scholarly journals Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data

2020 ◽  
Vol 5 (3) ◽  
pp. 138-146 ◽  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Credit Card ◽  
Imbalanced Data ◽  
Learning Systems ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Information Models ◽  
Mining Methods ◽  
Do So

:Most online customers use cards to pay for their purchases. As charge cards become the most mainstream strategy for installment, instances of misrepresentation relationship with it too increases. The primary goal of this venture is to be ready to perceive false exchanges from non-fake exchanges. In request to do so,primarily,data mining methods are utilized to examine the examples and attributes of deceitful and non-fake transactions.Then,machine learning systems are utilized to foresee the fake and non-fake exchanges automatically. Algorithms LR (Logistic Regression) is used. Therefore, the blend of AI and information mining procedures are utilized to distinguish the fake and non-fake exchanges by learning the examples of the information. Models are made utilizing these calculations and afterward precision,accuracy,recall are determined and an examination is made.

scholarly journals Ensemble Data Mining Methods

2008 ◽  
pp. 356-363 ◽  
Author(s):  
Nikunj C. Oza
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Prediction Accuracy ◽  
Ensemble Methods ◽  
Multiple Models ◽  
Machine Learning Methods ◽  
Ensemble Data ◽  
Basic Goal ◽  
Mining Methods ◽  
The Individual

Ensemble data mining methods, also known as committee methods or model combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: Each member of the committee should be as competent as possible, but the members should complement one another. If the members are not complementary, that is, if they always agree, then the committee is unnecessary — any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

scholarly journals Analisis Pola Pembelian Obat di Apotek Sekar Adi Menggunakan Metode Algoritma Apriori Depok

2020 ◽  
Vol 4 (1) ◽  
pp. 112
Author(s):  
Siti Awaliyah Rachmah Sutomo ◽  
Frisma Handayanna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Mining Methods ◽  
Using Data ◽  
Purchase Patterns

By using data mining methods can be processed to obtain information and assist in decision making, the amount of data on sales transactions in each drug purchase can cause a data accumulation and various problems, such as drug stock inventory, and sales transaction data, with Data mining techniques, the behavior of consumers in making transactions of drug purchase patterns can be analyzed, It can be known what drugs are commonly purchased by mostly people, the application of Apriori Algorithm is expected to help in forming a combination of itemset. The process of determining drug purchase patterns can be carried out by applying the Appriori algorithm method, determination of drug purchase patterns can be done by looking at the results of the consumer's tendency to buy drugs based on a combination of 3 itemset. By calculating the Analysis of High Frequency Patterns and the Formation of Association Rules, with a minimum of 30% support, there is a combination of 3 itemsset namely MOLAGIT PER TAB (M1), VIT C TABLET (V2), and PARACETAMOL 500 MG TABLET (P2) with 33.33 % support results obtained, and with minimum confidence of 65% there are 6 final association rules.

scholarly journals Prediction of Blood Donors Population Using Data Mining Classification Technique

2021 ◽  
pp. 25-27
Author(s):  
Aman Paul ◽  
Daljeet Singh
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Association Rules ◽  
User Interfaces ◽  
Relational Data ◽  
Easy Access ◽  
Data Mining Technique ◽  
Open Source Data ◽  
Using Data

Data mining is a technique that finds relationships and trends in large datasets to promote decision support. Classification is a data mining technique that maps data into predefined classes often referred as supervised learning because classes are determined before examining data. Different classification algorithms have been proposed for the effective classification of data. Among others, Weka is an open-source data mining software with which classification can be achieved. It is also well suited for developing new machine learning schemes. It allows users to quickly compare different machine learning methods on new datasets. It has several graphical user interfaces that enable easy access to the underlying functionality. CBA is a data mining tool which not only produces an accurate classifier for prediction, but it is also able to mine various forms of association rules. It has better classification accuracy and faster mining speed. It can build accurate classifiers from relational data and mine association rules from relational data and transactional data. CBA also has many other features like cross validation for evaluating classifiers and allows the user to view and to query the discovered rules.

scholarly journals Data Mining applied on Web Robots Detection: A Systematic Mapping

2021 ◽  
Author(s):  
Ramon Abilio ◽  
Cristiano Garcia ◽  
Victor Fernandes
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Algorithms ◽  
Web Server ◽  
Machine Learning Algorithms ◽  
Web Pages ◽  
Systematic Mapping ◽  
Mining Methods ◽  
Server Logs ◽  
Web Server Logs

Browsing on Internet is part of the world population’s daily routine. The number of web pages is increasing and so is the amount of published content (news, tutorials, images, videos) provided by them. Search engines use web robots to index web contents and to offer better results to their users. However, web robots have also been used for exploiting vulnerabilities in web pages. Thus, monitoring and detecting web robots’ accesses is important in order to keep the web server as safe as possible. Data Mining methods have been applied to web server logs (used as data source) in order to detect web robots. Then, the main objective of this work was to observe evidences of definition or use of web robots detection by analyzing web server-side logs using Data Mining methods. Thus, we conducted a systematic Literature mapping, analyzing papers published between 2013 and 2020. In the systematic mapping, we analyzed 34 studies and they allowed us to better understand the area of web robots detection, mapping what is being done, the data used to perform web robots detection, the tools, and algorithms used in the Literature. From those studies, we extracted 33 machine learning algorithms, 64 features, and 13 tools. This study is helpful for researchers to find machine learning algorithms, features, and tools to detect web robots by analyzing web server logs.

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