Data Mining Techniques and Applications

2018 ◽  
Vol 9 (1) ◽  
pp. 78-97 ◽  
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Unstructured Data ◽  
The Internet ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Extant Literature ◽  
Business Environments ◽  
Data Volume ◽  
Complete Set

Data mining has been gaining attention with the complex business environments, as a rapid increase of data volume and the ubiquitous nature of data in this age of the internet and social media. Organizations are interested in making informed decisions with a complete set of data including structured and unstructured data that originate both internally and externally. Different data mining techniques have evolved over the last two decades. To solve a wide variety of business problems, different data mining techniques are developed. Practitioners and researchers in industry and academia continuously develop and experiment varieties of data mining techniques. This article provides an overview of data mining techniques that are widely used in different fields to discover knowledge and solve business problems. This article provides an update on data mining techniques based on extant literature as of 2018. That might help practitioners and researchers to have a holistic view of data mining techniques.

scholarly journals A Survey of Data Mining Techniques on Information Networks

2018 ◽  
Vol 7 (2.6) ◽  
pp. 293
Author(s):  
Sadhana Kodali ◽  
Madhavi Dabbiru ◽  
B Thirumala Rao
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Networks ◽  
Information Network ◽  
Heterogeneous Information ◽  
Data Mining Techniques ◽  
Heterogeneous Information Networks ◽  
Web Objects ◽  
The Social ◽  
Social Media Network

An Information Network is the network formed by the interconnectivity of the objects formed due to the interaction between them. In our day-to-day life we can find these information networks like the social media network, the network formed by the interaction of web objects etc. This paper presents a survey of various Data Mining techniques that can be applicable to information networks. The Data Mining techniques of both homogeneous and heterogeneous information networks are discussed in detail and a comparative study on each problem category is showcased.

scholarly journals Mining on Social Media Data: To Determine the Personality of Unrevealed Person

2019 ◽  
Vol 8 (4) ◽  
pp. 8574-8577
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Mining ◽  
Social Media Data ◽  
Data Mining Techniques ◽  
Network Discovery ◽  
The Social ◽  
Mining Methods ◽  
Using Data ◽  
Media Data

The unavoidable utilization of online networking like Facebook is giving exceptional measures of social information. Information mining methods have been broadly used to separate learning from such information. The character of the person is predicted whether he is good or not by using data mining techniques from user self-made data. Mining methods are being broadly using to separate learning from such information, main examples for them are network discovery and slant investigation. Notwithstanding, there is still a lot of room to investigate as far as the occasion information (i.e., occasions with timestamps, for example, posting an inquiry, altering an article in Wikipedia, and remarking on a tweet. These occasions react users' personal conduct standards and working forms in the social media websites.

scholarly journals A Framework to process Text Data of Web Discussion Forums A Study of LisLinks

2019 ◽  
Vol 39 (06) ◽  
pp. 315-321
Author(s):  
Mohit Garg ◽  
Uma Kanjilal
Keyword(s):  
Social Media ◽  
Unstructured Data ◽  
The Internet ◽  
Discussion Forums ◽  
Text Data ◽  
Knowledge Repository ◽  
Social Media Platforms ◽  
Statistical Programming ◽  
Application Programming ◽  
Programming Interfaces

Nowadays, people use the internet for both seeking and disseminating information in a collaborative way on various social media platforms like Quora, Yahoo Answers, LisLinks Forum, etc. This social interaction on different topics makes these platforms as a knowledge repository. Evaluation of these repositories can help to understand various trends. However, this evaluation is a challenging task because of unstructured data and the unavailability of application programming interfaces for the harvesting of a dataset. This study presented a framework to harvest and pre-processing of data available on LisLinks Forum. The proposed framework is implemented using statistical programming language R. The fourteen metadata elements were defined for the discussion forums. The framework automatically harvest and pre-process relevant data of posts.

scholarly journals Research of various Data Mining Techniques for IoT Applications

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1083-1086
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Internet Of Things ◽  
Important Task ◽  
The Internet ◽  
Huge Amount ◽  
Data Mining Techniques ◽  
Amount Of Information ◽  
Iot Applications ◽  
Iot Devices

In recent years everything is connected and passing through the internet, but Internet of Things (IOT), which will change all aspects of our lives and future. While the things are connected to the internet, they will generate the huge amount of information which has to be processed. The information that gathered from various IoT devices has to be recognized and organized according to the environments of their type. To recognize and organize the data gathered from different things, the important task to be played is making things passing through different Data Mining Techniques (DMT). In this article, we mainly focus on analysis of various Data Mining Techniques over the data that has been generated by the IOT Devices which are connected over the internet using DBSCAN Technique. And also performed review over different Data Mining Techniques for Data Analysis

Mining Big Data and Streams

2018 ◽  
pp. 406-417
Author(s):  
Hoda Ahmed Abdelhafez
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Large Datasets ◽  
Lessons Learned ◽  
Velocity Data ◽  
Time Data ◽  
Data Mining Techniques ◽  
Complex Information ◽  
Very Large Datasets ◽  
Data Volume

Mining big data is getting a lot of attention currently because the businesses need more complex information in order to increase their revenue and gain competitive advantage. Therefore, mining the huge amount of data as well as mining real-time data needs to be done by new data mining techniques/approaches. This chapter will discuss big data volume, variety and velocity, data mining techniques and open source tools for handling very large datasets. Moreover, the chapter will focus on two industrial areas telecommunications and healthcare and lessons learned from them.

Data Mining Techniques for Distributed Denial of Service Attacks Detection in the Internet of Things

2016 ◽  
pp. 275-334 ◽  
Author(s):  
Pheeha Machaka ◽  
Fulufhelo Nelwamondo
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Denial Of Service ◽  
Current Data ◽  
The Internet ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Data Mining Techniques ◽  
The Internet Of Things

This chapter reviews the evolution of the traditional internet into the Internet of Things (IoT). The characteristics and application of the IoT are also reviewed, together with its security concerns in terms of distributed denial of service attacks. The chapter further investigates the state-of-the-art in data mining techniques for Distributed Denial of Service (DDoS) attacks targeting the various infrastructures. The chapter explores the characteristics and pervasiveness of DDoS attacks. It also explores the motives, mechanisms and techniques used to execute a DDoS attack. The chapter further investigates the current data mining techniques that are used to combat and detect these attacks, their advantages and disadvantages are explored. Future direction of the research is also provided.

Mining Big Data and Streams

2019 ◽  
pp. 94-107
Author(s):  
Hoda Ahmed Abdelhafez
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Large Datasets ◽  
Lessons Learned ◽  
Velocity Data ◽  
Time Data ◽  
Data Mining Techniques ◽  
Complex Information ◽  
Very Large Datasets ◽  
Data Volume

Mining big data is getting a lot of attention currently because businesses need more complex information in order to increase their revenue and gain competitive advantage. Therefore, mining the huge amount of data as well as mining real-time data needs to be done by new data mining techniques/approaches. This chapter will discuss big data volume, variety, and velocity, data mining techniques, and open source tools for handling very large datasets. Moreover, the chapter will focus on two industrial areas telecommunications and healthcare and lessons learned from them.

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