Intelligent Approach for Enhancing Prediction Issues in Scalable Data Mining

Support vector regression (SVR) is one of the supervised machine learning algorithms that can be exploited for prediction issues. The main enhancement issue of SVR is attempting to select a reliable parameter to assure the high performance of SVR. In this paper, the intelligent approach is based on integrating the enhanced particle swarm optimization PSO with the SVR to achieve the proper SVR parameters that are used to improve SVR performance. The enhanced PSO is performed by implementing parallelized linear time-variant acceleration coefficients (TVAC) and inertia weight (IW) of PSO, called PLTVACIW-PSO. The proposed approach is evaluated by performing the experimental comparisons of the proposed algorithm with eleven different algorithms. These comparisons are performed by applying the proposed algorithm and these algorithms to 21 different datasets varying in their scales.

A Dynamic and Scalable Decision Tree Based Mining of Educational Data

The explosive growth in the amount of data in the field of biology, education, environmental research, sensor network, stock market, weather forecasting and many more due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge. Hence data mining has become a research are with increasing importance. Since continuation in collection of more data at this scale, formalizing the process of big data analysis will become paramount. Given the vast amount of data are geographically spread across the globe, this means a very large number of models is generated, which raises problems on how to generalize knowledge in order to have a global view of the phenomena across the organization. This is applicable to web-based educational data. In this chapter, the new dynamic and scalable data mining approach has been discussed with educational data.

A Dynamic and Scalable Decision Tree Based Mining of Educational Data

The explosive growth in the amount of data in the field of biology, education, environmental research, sensor network, stock market, weather forecasting and many more due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge. Hence data mining has become a research are with increasing importance. Since continuation in collection of more data at this scale, formalizing the process of big data analysis will become paramount. Given the vast amount of data are geographically spread across the globe, this means a very large number of models is generated, which raises problems on how to generalize knowledge in order to have a global view of the phenomena across the organization. This is applicable to web-based educational data. In this chapter, the new dynamic and scalable data mining approach has been discussed with educational data.

Automated Identification of Child Abuse in Chat Rooms by Using Data Mining

Providing a safe environment for juveniles and children in online social networks is considered as one of the major factors of improving public safety. Due to the prevalence of the online conversations, mitigating the undesirable effects of child abuse in cyber space has become inevitable. Using automatic ways to combat this kind of crime is challenging and demands efficient and scalable data mining techniques. The problem can be casted as a combination of textual preprocessing in data/text mining and pattern classification in machine learning. This chapter covers different data mining methods including preprocessing, feature extraction and the popular ways of feature enrichment through extracting sentiments and emotional features. A brief tutorial on classification algorithms in the domain of automated predator identification is also presented through the chapter. Finally, the discussion is summarized and the challenges and open issues in this application domain are discussed.