scholarly journals Resource Allocation in Wireless Networks

2011 ◽  
pp. 955-959
Author(s):  
Dimitrios Katsaros ◽  
Gökhan Yavas ◽  
Alexandros Nanopoulos ◽  
Murat Karakaya ◽  
Özgür Ulusoy ◽  
...  
Keyword(s):  
Data Mining ◽  
Resource Allocation ◽  
Wireless Networks ◽  
Data Collection ◽  
Scientific Data ◽  
Data Mining Techniques ◽  
Vast Amount ◽  
The Past

During the past years, we have witnessed an explosive growth in our capabilities to both generate and collect data. Advances in scientific data collection, the computerization of many businesses, and the recording (logging) of clients’ accesses to networked resources have generated a vast amount of data. Various data mining techniques have been proposed and widely employed to discover valid, novel and potentially useful patterns in these data.

Tree and Graph Mining

2011 ◽  
pp. 1140-1145
Author(s):  
Dimitrios Katsaros ◽  
Yannis Manolopoulos
Keyword(s):  
Data Mining ◽  
Graph Mining ◽  
Data Mining Techniques ◽  
The Past

During the past decade, we have witnessed an explosive growth in our capabilities to both generate and collect data. Various data mining techniques have been proposed and widely employed to discover valid, novel and potentially useful patterns in these data. Data mining involves the discovery of patterns, associations, changes, anomalies, and statistically significant structures and events in huge collections of data.

The Application of Data-Mining to Recommender Systems

2011 ◽  
pp. 45-50 ◽  
Author(s):  
J. Ben Schafer
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Digital Libraries ◽  
Buying Behavior ◽  
Data Mining Techniques ◽  
The Past ◽  
A Site ◽  
Product Description

In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. Such systems have become powerful tools in domains from electronic commerce to digital libraries and knowledge management. For example, a consumer of just about any major online retailer who expresses an interest in an item – either through viewing a product description or by placing the item in his “shopping cart” – will likely receive recommendations for additional products. These products can be recommended based on the top overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the consumer as a prediction for future buying behavior. This paper will address the technology used to generate recommendations, focusing on the application of data mining techniques.

Tools for Aggregating, Analyzing and Mining Combinatorial Data

2009 ◽  
Vol 1159 ◽  
Author(s):  
Wesley Jones ◽  
Changwon Suh ◽  
Peter A Graf ◽  
Daniel Korytina ◽  
Craig Swank ◽  
...  
Keyword(s):  
Data Mining ◽  
Principal Component ◽  
Analysis Data ◽  
Scientific Data ◽  
Data Management System ◽  
Multiple Sources ◽  
Data Mining Techniques ◽  
Conducting Oxides ◽  
Structure And Property ◽  
Combinatorial Data

AbstractWe demonstrate how data mining techniques can be applied to complex combinatorial data sets and how data from multiple sources can be aggregated via the developed scientific data management system. An example is shown for the case of aggregated combinatorial data for the study of composition, processing, structure, and property relationships of transparent conducting oxides by applying data mining techniques such as principal component analysis. Data mappings of mined results are shown to effectively enable visualization of data trends, identification of anomalies in Fourier transform infrared spectroscopy patterns, and scientifically interesting libraries and spectral regions.

Distributed Association Rule Mining

2011 ◽  
pp. 403-407
Author(s):  
Mafruz Zaman Ashrafi ◽  
David Taniar ◽  
Kate A. Smith
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Digital Data ◽  
Computationally Efficient ◽  
Rule Mining ◽  
Interactive Process ◽  
Data Mining Techniques ◽  
The Past ◽  
Exploratory Data ◽  
Distributed Association

Data mining is an iterative and interactive process that explores and analyzes voluminous digital data to discover valid, novel, and meaningful patterns (Mohammed, 1999). Since digital data may have terabytes of records, data mining techniques aim to find patterns using computationally efficient techniques. It is related to a subarea of statistics called exploratory data analysis. During the past decade, data mining techniques have been used in various business, government, and scientific applications.

Application of Data Mining to Recommender Systems

2011 ◽  
pp. 44-48 ◽  
Author(s):  
J. Ben Schafer
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Digital Libraries ◽  
Buying Behavior ◽  
Data Mining Techniques ◽  
The Past ◽  
A Site ◽  
Product Description

In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. Such systems have become powerful tools in domains from electronic commerce to digital libraries and knowledge management. For example, a consumer of just about any major online retailer who expresses an interest in an item – either through viewing a product description or by placing the item in his “shopping cart” – will likely receive recommendations for additional products. These products can be recommended based on the top overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the consumer as a prediction for future buying behavior. This paper will address the technology used to generate recommendations, focusing on the application of data mining techniques.

A Review of Data Mining Techniques and Applications

2017 ◽  
Vol 21 (1) ◽  
pp. 31-48 ◽  
Author(s):  
Ratchakoon Pruengkarn ◽  
◽  
Kok Wai Wong ◽  
Chun Che Fung
Keyword(s):  
Data Mining ◽  
Health Informatics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Huge Number ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
The Past ◽  
Number Of Publications

Data mining is the analytics and knowledge discovery process of analyzing large volumes of data from various sources and transforming the data into useful information. Various disciplines have contributed to its development and is becoming increasingly important in the scientific and industrial world. This article presents a review of data mining techniques and applications from 1996 to 2016. Techniques are divided into two main categories: predictive methods and descriptive methods. Due to the huge number of publications available on this topic, only a selected number are used in this review to highlight the developments of the past 20 years. Applications are included to provide some insights into how each data mining technique has evolved over the last two decades. Recent research trends focus more on large data sets and big data. Recently there have also been more applications in area of health informatics with the advent of newer algorithms.

Research on Data Mining Based on Apriori Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1675-1679
Author(s):  
Pei Ji Wang ◽  
Yu Lin Zhao
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Association Rules ◽  
Frequent Itemsets ◽  
Scientific Data ◽  
Apriori Algorithm ◽  
Data Table ◽  
Large Databases ◽  
Mining Association Rules ◽  
Mining Frequent Itemsets

With the availability of inexpensive storage and the progress in data collection tools, many organizations have created large databases of business and scientific data, which create an imminent need and great opportunities for mining interesting knowledge from data.Mining association rules is an important topic in the data mining research. In the paper, research mining frequent itemsets algorithm based on recognizable matrix and mining association rules algorithm based on improved measure system, the above method is used to mine association rules to the students’ data table under Visual FoxPro 6.0.

scholarly journals Use of Data Mining Techniques in Human Resource Management

2017 ◽  
Vol 1 (1) ◽  
pp. 41-46
Author(s):  
Okaile R. Marumo ◽  
Tumisang Angela Mmopelwa
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Transformational Change ◽  
Decision Making Process ◽  
Data Mining Techniques ◽  
The Past ◽  
Use Of Data

In the past few years, Analytics has rapidly risen in among organizations within the field of human resource management. To the present date, however, Human Resource Analytics has not been subject to a lot of scrutiny from educational researchers. The aim of this paper is so to look at Different Mining Techniques could be implemented in the HR Department to enhance or support their decision making process. This will improve existing practices of HR analytics and will deliver transformational change indeed

scholarly journals Survivability Strategies for Emerging Wireless Networks With Data Mining Techniques: a Case Study With NetLogo and RapidMiner

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 27958-27970 ◽  
Author(s):  
Ivan Garcia-Magarino ◽  
Geraldine Gray ◽  
Raquel Lacuesta ◽  
Jaime Lloret
Keyword(s):  
Data Mining ◽  
Wireless Networks ◽  
Data Mining Techniques
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