Data Mining in Practice

2008 ◽  
pp. 2273-2280
Author(s):  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
New Technologies ◽  
Research Area ◽  
Degree Of Confidence

There is an explosion in the amount of data that organizations generate, collect, and store. Organizations are gradually relying more on new technologies to access, analyze, summarize, and interpret information intelligently and automatically. Data mining, therefore, has become a research area with increased importance (Amaratunga & Cabrera, 2004). Data mining is the search for valuable information in large volumes of data (Hand, Mannila, & Smyth, 2001). It can discover hidden relationships, patterns, and interdependencies, and generate rules to predict the correlations, which can help the organizations make critical decisions faster or with a greater degree of confidence (Gargano & Raggad, 1999).

Data Mining

2011 ◽  
pp. 921-926
Author(s):  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
Electronic Commerce ◽  
New Technologies ◽  
Research Area ◽  
Future Research ◽  
Data Mining Techniques ◽  
Wide Range ◽  
Degree Of Confidence ◽  
Common Application ◽  
Existing Data

There is an explosion in the amount of data that organizations generate, collect, and store. Organizations are gradually relying more on new technologies to access, analyze, summarize, and interpret information intelligently. Data mining, therefore, has become a research area with increased importance (Amaratunga & Cabrera, 2004). Data mining is the search for valuable information in large volumes of data (Hand, Mannila, & Smyth, 2001). It can discover hidden relationships, patterns, and interdependencies and generate rules to predict the correlations, which can help the organizations make critical decisions faster or with a greater degree of confidence (Gargano & Ragged, 1999). There is a wide range of data mining techniques, which has been successfully used in many applications. This article is an attempt to provide an overview of existing data mining applications. The article begins by explaining the key tasks that data mining can achieve. It then moves to discuss applications domains that data mining can support. The article identifies three common application domains, including bioinformatics, electronic commerce, and search engines. For each domain, how data mining can enhance the functions will be described. Subsequently, the limitations of current research will be addressed, followed by a discussion of directions for future research.

Data Mining in Practice

2005 ◽  
pp. 723-728
Author(s):  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
New Technologies ◽  
Research Area ◽  
Degree Of Confidence

There is an explosion in the amount of data that organizations generate, collect, and store. Organizations are gradually relying more on new technologies to access, analyze, summarize, and interpret information intelligently and automatically. Data mining, therefore, has become a research area with increased importance (Amaratunga & Cabrera, 2004). Data mining is the search for valuable information in large volumes of data (Hand, Mannila, & Smyth, 2001). It can discover hidden relationships, patterns, and interdependencies, and generate rules to predict the correlations, which can help the organizations make critical decisions faster or with a greater degree of confidence (Gargano & Raggad, 1999).

DATA MINING AS A MEANS OF QUALITY CONTROL IN PRODUCTION AND ORGANIZATION MANAGEMENT

2020 ◽  
Vol 1 (5) ◽  
pp. 130-138
Author(s):  
L. S. ZVYAGIN ◽  
Keyword(s):  
Data Mining ◽  
Quality Control ◽  
New Technologies ◽  
Data Interpretation ◽  
Organization Management

The article deals with data mining (IAD), which is widely used both in business and in various studies. IAD methods are used to create new ways to solve problems of forecasting, segmentation, data interpretation, etc. The problems to be solved by creating new technologies and methods of IAD are analyzed.

scholarly journals 3D AutoSysLab Prototype - A Social, Immersive and Mixed Reality Approach for Collaborative Learning Environments

2012 ◽  
Vol 2 (2) ◽  
pp. 15 ◽  
Author(s):  
Frederico Menine Schaf ◽  
Suenoni Paladini ◽  
Carlos Eduardo Pereira
Keyword(s):  
Virtual Environments ◽  
Virtual Worlds ◽  
New Technologies ◽  
Mixed Reality ◽  
Research Area ◽  
Learning Objects ◽  
Traditional Learning ◽  
Web Technologies ◽  
3D Virtual Worlds ◽  
Great Acceptance

<span style="color: #000000;"><span style="font-family: Times New Roman,serif;"><span style="font-size: x-small;">Recent evolutions of social networks, virtual environments, Web technologies and 3D virtual worlds motivate the adoption of new technologies in education, opening successive innovative possibilities. These technologies (or tools) can be employed in distance education scenarios, or can also enhance traditional learning-teaching (blended or hybrid learning scenario). It is known and a wide advocated issue that laboratory practice is essential to technical education, foremost in engineering. In order to develop a feasible implementation to this research area, a prototype was developed, called 3DAutoSysLab, in which a metaverse is used as social collaborative interface, experiments (real or simulated) are linked to virtual objects, learning objects are displayed as interactive medias, and guiding/feedback are supported via an autonomous tutoring system based on user's interaction data mining. This prototype is under test, but preliminary applied results indicate great acceptance and increase of motivation of students.</span></span></span>

scholarly journals A qualitative survey on frequent subgraph mining

2018 ◽  
Vol 8 (1) ◽  
pp. 194-209 ◽  
Author(s):  
Büsra Güvenoglu ◽  
Belgin Ergenç Bostanoglu
Keyword(s):  
Data Mining ◽  
Graph Mining ◽  
Research Area ◽  
Heterogeneous Data ◽  
Graph Representation ◽  
Frequent Subgraph Mining ◽  
Subgraph Mining ◽  
Frequent Subgraph ◽  
Input Type ◽  
Frequent Subgraphs

AbstractData mining is a popular research area that has been studied by many researchers and focuses on finding unforeseen and important information in large databases. One of the popular data structures used to represent large heterogeneous data in the field of data mining is graphs. So, graph mining is one of the most popular subdivisions of data mining. Subgraphs that are more frequently encountered than the user-defined threshold in a database are called frequent subgraphs. Frequent subgraphs in a database can give important information about this database. Using this information, data can be classified, clustered and indexed. The purpose of this survey is to examine frequent subgraph mining algorithms (i) in terms of frequent subgraph discovery process phases such as candidate generation and frequency calculation, (ii) categorize the algorithms according to their general attributes such as input type, dynamicity of graphs, result type, algorithmic approach they are based on, algorithmic design and graph representation as well as (iii) to discuss the performance of algorithms in comparison to each other and the challenges faced by the algorithms recently.

scholarly journals Health Threats in the European Region and Their Economic Impact: Lessons for Ukraine

2020 ◽  
pp. 70-77
Author(s):  
Nataliia Letunovska ◽  
Anna Rosokhata ◽  
Saher Liudmyla ◽  
Valeriia Lazorenko
Keyword(s):  
Economic Impact ◽  
New Technologies ◽  
Welfare Economics ◽  
European Region ◽  
Negative Consequences ◽  
Consumer Sentiment ◽  
Global Trends ◽  
Economic Sphere ◽  
Degree Of Confidence ◽  
The Impact

The article considers various aspects of the impact of the COVID-19 pandemic on the economic sphere of the countries of the European region. They write about some specific approaches to the government's influence on leveling the negative consequences of the spread of the pandemic within countries. The authors conclude that COVID-19 hurts the level of trust in society and the degree of confidence in consumer sentiment. Simultaneously, unforeseen events such as COVID-19 stimulate the emergence and accelerated introduction of new technologies. It is further generalized that Ukraine must take into account all global trends in the implementation of economic measures, as well as follow the mainstream of technologies that are increasingly widespread in society, in particular the concept of welfare economics.

scholarly journals Predicting Academic Course Preference using Hadoop

2019 ◽  
Vol 8 (4) ◽  
pp. 2527-2530
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
New Technologies ◽  
Parallel Applications ◽  
Distributed File System ◽  
Industrial Training ◽  
Effective Learning ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Mining Tools

These days new technologies have been introduced by this new academic trends also have been came into existence into the education system. And this leads to huge amounts of data which makes a big challenge for the students to store the preferred course. For this many data mining tools have been invented to convert the unregulated data into structured format to understand the meaningful information. As we know that Hadoop is a distributed file system which is used to hold huge amounts of data this stores the files in a redundant fashion across multiple machines. Due to this it leads to failure and parallel applications do not work. To avoid this problem we are using Mapreduce for decision making of students in order to choose their preferred course for industrial training purpose for their effective learning techniques to increase their knowledge and capability.

scholarly journals The comparative study of text documents clustering algorithms

2015 ◽  
Vol 16 (SE) ◽  
pp. 133-138
Author(s):  
Mohammad Eiman Jamnezhad ◽  
Reza Fattahi
Keyword(s):  
Data Mining ◽  
Dna Analysis ◽  
Clustering Algorithms ◽  
Research Area ◽  
Large Set ◽  
Text Documents ◽  
Web Documents ◽  
Significant Research ◽  
The Comparative Study ◽  
F Measure

Clustering is one of the most significant research area in the field of data mining and considered as an important tool in the fast developing information explosion era.Clustering systems are used more and more often in text mining, especially in analyzing texts and to extracting knowledge they contain. Data are grouped into clusters in such a way that the data of the same group are similar and those in other groups are dissimilar. It aims to minimizing intra-class similarity and maximizing inter-class dissimilarity. Clustering is useful to obtain interesting patterns and structures from a large set of data. It can be applied in many areas, namely, DNA analysis, marketing studies, web documents, and classification. This paper aims to study and compare three text documents clustering, namely, k-means, k-medoids, and SOM through F-measure.

Comparison of Linguistic Summaries and Fuzzy Functional Dependencies Related to Data Mining

2014 ◽  
pp. 174-203 ◽  
Author(s):  
Miroslav Hudec ◽  
Miljan Vučetić ◽  
Mirko Vujošević
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Relational Databases ◽  
Missing Values ◽  
Expert Knowledge ◽  
Real Data ◽  
Research Area ◽  
Functional Dependencies ◽  
Useful Knowledge ◽  
Important Research Area

Data mining methods based on fuzzy logic have been developed recently and have become an increasingly important research area. In this chapter, the authors examine possibilities for discovering potentially useful knowledge from relational database by integrating fuzzy functional dependencies and linguistic summaries. Both methods use fuzzy logic tools for data analysis, acquiring, and representation of expert knowledge. Fuzzy functional dependencies could detect whether dependency between two examined attributes in the whole database exists. If dependency exists only between parts of examined attributes' domains, fuzzy functional dependencies cannot detect its characters. Linguistic summaries are a convenient method for revealing this kind of dependency. Using fuzzy functional dependencies and linguistic summaries in a complementary way could mine valuable information from relational databases. Mining intensities of dependencies between database attributes could support decision making, reduce the number of attributes in databases, and estimate missing values. The proposed approach is evaluated with case studies using real data from the official statistics. Strengths and weaknesses of the described methods are discussed. At the end of the chapter, topics for further research activities are outlined.

Educational Data Mining

2020 ◽  
pp. 70-82
Author(s):  
Aslıhan Tüfekci ◽  
Esra Ayça Güzeldereli Yılmaz
Keyword(s):  
Data Mining ◽  
Learning Styles ◽  
Educational Data Mining ◽  
Research Area ◽  
Point Of View ◽  
Primary Sources ◽  
Systematic Mapping Study ◽  
Input Output ◽  
Learning Practices ◽  
Process Elements

The education-training process and all activities related to it have the power to direct the future of societies. From this point of view, the process should be analyzed frequently in terms of input, output, and other process elements. Educational data mining is a multidisciplinary research area that develops methods and techniques for discovering data derived from various information systems used in education. It contributes to the understanding of the learning styles of learners and enables data-driven decision making to develop existing learning practices and learning materials. The number of academic and technical research on educational data mining is on the rise, and this has led to the need to systematically categorize the existing practices. This systematic mapping study was conducted to provide an overview of the current work on educational data mining and its results are based on 153 primary sources including journal papers, articles published in magazines, conference and symposium papers, theses, and others.

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