Distributed Knowledge Acquisition Basing on Integration of Data Mining and Text Mining Methods and Their Usage with AT-TECHNOLOGY Workbench

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.

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Augmented Reality and Mobile Consumers

Managerial Challenges and Social Impacts of Virtual and Augmented Reality - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-2874-7.ch005 ◽

2020 ◽

pp. 76-94

Author(s):

Zehra Nur Canbolat ◽

Fatih Pinarbasi

Keyword(s):

Data Mining ◽

Augmented Reality ◽

Text Mining ◽

Mobile Application ◽

Unstructured Data ◽

Consumer Evaluations ◽

Theoretical Contribution ◽

Mining Methods ◽

The Uk ◽

Product Perception

In this chapter, consumer perceptions of augmented reality mobile applications will be emphasized and the analysis will be carried out through the mobile application markets of two different countries. In the research, the top 20 applications were selected from the UK and USA mobile application markets and the last consumer evaluations regarding these applications were obtained. In accordance with the purpose of the research, text mining methods were used to evaluate the expressions of consumers, since data mining methodologies can contribute to a better understanding of unstructured data. In the research, top words, bigram, and trigram are used in consumer comments. Then sentiment analysis method is employed to determine the emotions in consumer comments. Authors conclude that both markets have positive polarities. While the study provides a theoretical contribution in terms of consumer evaluations and new product perception, it also contributes to the sector in terms of expressions and evaluations used by consumers.

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Text Mining Perspectives in Microarray Data Mining

ISRN Computational Biology ◽

10.1155/2013/159135 ◽

2013 ◽

Vol 2013 ◽

pp. 1-5 ◽

Cited By ~ 1

Author(s):

Jeyakumar Natarajan

Keyword(s):

Gene Expression ◽

Data Mining ◽

Text Mining ◽

Gene Expression Data ◽

Microarray Data ◽

Machine Learning Algorithms ◽

Microarray Data Analysis ◽

Expression Data ◽

Related Data ◽

Mining Methods

Current microarray data mining methods such as clustering, classification, and association analysis heavily rely on statistical and machine learning algorithms for analysis of large sets of gene expression data. In recent years, there has been a growing interest in methods that attempt to discover patterns based on multiple but related data sources. Gene expression data and the corresponding literature data are one such example. This paper suggests a new approach to microarray data mining as a combination of text mining (TM) and information extraction (IE). TM is concerned with identifying patterns in natural language text and IE is concerned with locating specific entities, relations, and facts in text. The present paper surveys the state of the art of data mining methods for microarray data analysis. We show the limitations of current microarray data mining methods and outline how text mining could address these limitations.

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TEXT AND DATA MINING TECHNIQUES IN ASPECT OF KNOWLEDGE ACQUISITION FOR DECISION SUPPORT SYSTEM IN CONSTRUCTION INDUSTRY / DUOMENŲ RINKIMO METODAI STATYBOS SPRENDIMŲ PARAMOS SISTEMAI

Technological and Economic Development of Economy ◽

10.3846/tede.2010.14 ◽

2010 ◽

Vol 16 (2) ◽

pp. 219-232 ◽

Cited By ~ 25

Author(s):

Marcin Gajzler

Keyword(s):

Data Mining ◽

Decision Support ◽

Text Mining ◽

Knowledge Acquisition ◽

Construction Industry ◽

Support System ◽

Text Documents ◽

Fundamental Feature ◽

Mining Technique ◽

Text And Data Mining

This article presents the possibilities of using mining techniques in building Decision Support Systems. One of the biggest problems is the issue of gaining data and knowledge, their mutual representation and reciprocal usage. Data and knowledge make up the resources of the system and are its key link. It has been estimated that 70% to 80% of the sources available for general use are text documents. The text mining technique is defined as a process aiming to extract previously unknown information from text resources (e.g. technological cards). The fundamental feature of text mining is the ability to converse text documents in formal form, which opens up great possibilities of conducting further analysis. This article presents chosen IT tools using text mining technique, along with the elements of the text mining analysis. The main objectives are the simplification of the process of knowledge acquisition, its automation and shortening as well as the creation of ready‐made models containing knowledge. Previous tests with knowledge acquisition (surveys, questionnaires) were time‐consuming and exacting for experts. Santrauka Straipsnyje pateikiamos informacijos rinkimo metodu pritaikymo galimybės sprendimų paramos sistemoms statyboje. Daugiausia problemų sukelia informacijos gavimas, tinkamas jos atvaizdavimas ir naudojimas. Duomenys yra pagrindinis sistemos išteklius. Nustatyta, kad nuo 70 iki 80 % visu turimų bendrojo naudojimo informacijos šaltinių yra tekstiniai dokumentai. Tekstines informacijos rinkimo technika yra suprantama kaip procesas, kuriuo siekiama išgauti anksčiau nežinoma informacija iš tekstiniu dokumentu (pavyzdžiui, technologiniu kortelių). Pagrindine šios technikos savybė ‐ galimybė tekstinių dokumentų informacija pateikti formalizuota forma, tai atveria plačiu galimybių tolesnei analizei. Šiame straipsnyje pateikiamos pasirinktos IT priemonės, naudojamos tekstinei informacijai rinkti. Autoriaus tikslas ‐ su paprastinti informacijos rinkimą, ji automatizuoti ir sutrumpinti, sukurti informacija apimančius modelius. Ankstesni informacijos kaupimo metodai (apklausos, anketos) reikalavo daug ekspertų darbo ir laiko.

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Applications of Pattern Discovery Using Sequential Data Mining

Data Mining ◽

10.4018/978-1-4666-2455-9.ch048 ◽

2013 ◽

pp. 947-969

Author(s):

Manish Gupta ◽

Jiawei Han

Keyword(s):

Data Mining ◽

Text Mining ◽

Intrusion Detection ◽

Pattern Mining ◽

Pattern Discovery ◽

Sequential Pattern Mining ◽

Web Usage Mining ◽

Sequential Pattern ◽

Sequential Data ◽

Mining Methods

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.

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Textual Data Mining For Knowledge Discovery and Data Classification: A Comparative Study

European Scientific Journal ESJ ◽

10.19044/esj.2017.v13n21p429 ◽

2017 ◽

Vol 13 (21) ◽

pp. 429

Author(s):

Nadeem Ur-Rahman

Keyword(s):

Data Mining ◽

Text Mining ◽

Language Processing ◽

Business Processes ◽

Decision Makers ◽

Text Documents ◽

Textual Data ◽

Statistical Natural Language Processing ◽

Mining Methods ◽

Many Sources

Business Intelligence solutions are key to enable industrial organisations (either manufacturing or construction) to remain competitive in the market. These solutions are achieved through analysis of data which is collected, retrieved and re-used for prediction and classification purposes. However many sources of industrial data are not being fully utilised to improve the business processes of the associated industry. It is generally left to the decision makers or managers within a company to take effective decisions based on the information available throughout product design and manufacture or from the operation of business or production processes. Substantial efforts and energy are required in terms of time and money to identify and exploit the appropriate information that is available from the data. Data Mining techniques have long been applied mainly to numerical forms of data available from various data sources but their applications to analyse semi-structured or unstructured databases are still limited to a few specific domains. The applications of these techniques in combination with Text Mining methods based on statistical, natural language processing and visualisation techniques could give beneficial results. Text Mining methods mainly deal with document clustering, text summarisation and classification and mainly rely on methods and techniques available in the area of Information Retrieval (IR). These help to uncover the hidden information in text documents at an initial level. This paper investigates applications of Text Mining in terms of Textual Data Mining (TDM) methods which share techniques from IR and data mining. These techniques may be implemented to analyse textual databases in general but they are demonstrated here using examples of Post Project Reviews (PPR) from the construction industry as a case study. The research is focused on finding key single or multiple term phrases for classifying the documents into two classes i.e. good information and bad information documents to help decision makers or project managers to identify key issues discussed in PPRs which can be used as a guide for future project management process.

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