Augmented Reality and Mobile Consumers

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.

Applications of Pattern Discovery Using Sequential Data Mining

2012 ◽  
pp. 1-23 ◽  
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.

scholarly journals Text Mining Perspectives in Microarray Data Mining

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
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.

scholarly journals Data-Mining Opportunities for Small and Medium Enterprises with Official Statistics in the UK

2016 ◽  
Vol 32 (4) ◽  
pp. 849-865 ◽  
Author(s):  
Shirley Y. Coleman
Keyword(s):  
Data Mining ◽  
Case Studies ◽  
Data Analytics ◽  
Small And Medium Enterprises ◽  
Open Data ◽  
Official Statistics ◽  
Business Opportunities ◽  
Mining Methods ◽  
The Uk ◽  
Medium Enterprises

Abstract There is a growing interest in data amongst small and medium enterprises (SMEs). This article looks at ways in which SMEs can combine their internal company data with open data, such as official statistics, and thereby enhance their business opportunities. Case studies are given as illustrations of the statistical and data-mining methods involved in such integrated data analytics. The article considers the barriers that prevent more SMEs from benefitting in this field and appraises some of the initiatives that are aimed at helping to overcome them. The discussion emphasizes the importance of bringing people together from the business, IT, and statistical worlds and suggests ways for statisticians to make a greater impact.

An Integrated Approach to Encompass Big Data Cloud Adoption Using Data Mining

2020 ◽  
Vol 17 (8) ◽  
pp. 3383-3388
Author(s):  
S. Nandhini ◽  
Prangad Khanna ◽  
Sachit Jain ◽  
Raunak Pal
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Integrated Approach ◽  
Media Analysis ◽  
Unstructured Data ◽  
Data Sets ◽  
Social Media Analysis ◽  
Comprehensive Information ◽  
Software Markets ◽  
Using Data

Text Mining is one of the most censorious techniques for analysis of data. It processes the unstructured data which has been found to hold nearly 80% of the world’s data. In the present time a majority of industries and big firms use and store massive amount of data sets. The information gets stored in the data Warehouses, and cloud platforms respectively. The data getting stored shows an exponential growth each time since multiple amount of new data is said to get stored every minute from distinct sources. Due to heavy presence of information stored in the platforms it becomes difficult to gather and extract the most relevant part from the data. Thus, text mining and data mining techniques are applied with algorithms such as CNN and RNN supporting it. The algorithm performs deep analysis of data and presents the most relevant piece of information, removing the less important ones. The relevance of the project comes into existence when it’s seen to be integrated into industries such as software markets, social media analysis and formulating of market reports. Thus, providing an extremely comprehensive information which would enhance scope and application of the data getting stored.

Applications of Pattern Discovery Using Sequential Data Mining

Data Mining ◽  
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.

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

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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