摘要 :
This paper presents recent emerging and active data mining research fields in the period between 2013 and 2015. The research fields include education, healthcare, web, data stream, and big data. The paper starts by explaining the ...
展开
This paper presents recent emerging and active data mining research fields in the period between 2013 and 2015. The research fields include education, healthcare, web, data stream, and big data. The paper starts by explaining the conducted research methodology and basic data mining processes and methods. Then, for each research field, the processes, algorithms, applications, and challenges from the relevant surveys and research are reported. Finally, the paper concludes with challenges encountering data mining research and future research directions that shows possible research gaps in the field and open the door for future research.
收起
摘要 :
This paper presents recent emerging and active data mining research fields in the period between 2013 and 2015. The research fields include education, healthcare, web, data stream, and big data. The paper starts by explaining the ...
展开
This paper presents recent emerging and active data mining research fields in the period between 2013 and 2015. The research fields include education, healthcare, web, data stream, and big data. The paper starts by explaining the conducted research methodology and basic data mining processes and methods. Then, for each research field, the processes, algorithms, applications, and challenges from the relevant surveys and research are reported. Finally, the paper concludes with challenges encountering data mining research and future research directions that shows possible research gaps in the field and open the door for future research.
收起
摘要 :
Modern universities are collecting and keeping large volumes of data, referring to their students, the organization and management of the educational process, and other managerial issues, hi most cases, these are unique types of d...
展开
Modern universities are collecting and keeping large volumes of data, referring to their students, the organization and management of the educational process, and other managerial issues, hi most cases, these are unique types of data and their proper analysis could significantly support the university management in the decision making process and in the university policy development. The data mining project, initiated at one of the biggest and most prestigious Bulgarian universities, is presented in this paper. The project objective is to develop the profile of university students according to the university performance results based on their pre-university characteristics. The obtained knowledge will support the university management in better defining the university marketing strategy, targeting and attracting the most appropriate and promising students. Data mining is a new discipline in the ICT field. The implementation of sophisticated data mining methods and techniques for data analysis is a very innovative approach for Bulgarian organizations. The initiated project is the first attempt to apply data mining for processing university data in order to support decision making in the Bulgarian educational sector.
收起
摘要 :
Modern universities are collecting and keeping large volumes of data, referring to their students, the organization and management of the educational process, and other managerial issues, hi most cases, these are unique types of d...
展开
Modern universities are collecting and keeping large volumes of data, referring to their students, the organization and management of the educational process, and other managerial issues, hi most cases, these are unique types of data and their proper analysis could significantly support the university management in the decision making process and in the university policy development. The data mining project, initiated at one of the biggest and most prestigious Bulgarian universities, is presented in this paper. The project objective is to develop the profile of university students according to the university performance results based on their pre-university characteristics. The obtained knowledge will support the university management in better defining the university marketing strategy, targeting and attracting the most appropriate and promising students. Data mining is a new discipline in the ICT field. The implementation of sophisticated data mining methods and techniques for data analysis is a very innovative approach for Bulgarian organizations. The initiated project is the first attempt to apply data mining for processing university data in order to support decision making in the Bulgarian educational sector.
收起
摘要 :
The present paper describes the framework for creating data mining algorithms from thread-safe functional blocks. This framework requirements decomposition of algorithms into independently functioning blocks. These blocks must hav...
展开
The present paper describes the framework for creating data mining algorithms from thread-safe functional blocks. This framework requirements decomposition of algorithms into independently functioning blocks. These blocks must have unified interfaces and implement pure functions. The framework allows create new data mining algorithms from existing blocks and improves the existing algorithms by optimizing single blocks or the whole structure of the algorithms. This becomes possible due to a number of important properties such as thread-safety inherent in pure functions and hence functional blocks.
收起
摘要 :
The present paper describes the framework for creating data mining algorithms from thread-safe functional blocks. This framework requirements decomposition of algorithms into independently functioning blocks. These blocks must hav...
展开
The present paper describes the framework for creating data mining algorithms from thread-safe functional blocks. This framework requirements decomposition of algorithms into independently functioning blocks. These blocks must have unified interfaces and implement pure functions. The framework allows create new data mining algorithms from existing blocks and improves the existing algorithms by optimizing single blocks or the whole structure of the algorithms. This becomes possible due to a number of important properties such as thread-safety inherent in pure functions and hence functional blocks.
收起
摘要 :
The article describes extension of λ-calculation for creation of parallel data mining algorithms. The proposed approach uses presentation of the algorithm as a consequence of pure functions with unified interfaces. For parallel e...
展开
The article describes extension of λ-calculation for creation of parallel data mining algorithms. The proposed approach uses presentation of the algorithm as a consequence of pure functions with unified interfaces. For parallel execution we use special function that allows to change a structure of the algorithm and to implement various strategies for processing of data set and model.
收起
摘要 :
The article describes extension of λ-calculation for creation of parallel data mining algorithms. The proposed approach uses presentation of the algorithm as a consequence of pure functions with unified interfaces. For parallel e...
展开
The article describes extension of λ-calculation for creation of parallel data mining algorithms. The proposed approach uses presentation of the algorithm as a consequence of pure functions with unified interfaces. For parallel execution we use special function that allows to change a structure of the algorithm and to implement various strategies for processing of data set and model.
收起
摘要 :
The article describes a approach of parallel data mining algorithms to be executed on multicore processors of various architecture. The suggested method presents an algorithm as a consequence of pure functions with unified interfa...
展开
The article describes a approach of parallel data mining algorithms to be executed on multicore processors of various architecture. The suggested method presents an algorithm as a consequence of pure functions with unified interfaces. For parallel execution additional functions are introduced to share data and models between the parallel threads. Besides such functions allow to obtain various parallel algorithm structures and implement various strategies of execution for different environment conditions. Application of the described method is illustrated through algorithm Naïve Bayes.
收起
摘要 :
The article describes a approach of parallel data mining algorithms to be executed on multicore processors of various architecture. The suggested method presents an algorithm as a consequence of pure functions with unified interfa...
展开
The article describes a approach of parallel data mining algorithms to be executed on multicore processors of various architecture. The suggested method presents an algorithm as a consequence of pure functions with unified interfaces. For parallel execution additional functions are introduced to share data and models between the parallel threads. Besides such functions allow to obtain various parallel algorithm structures and implement various strategies of execution for different environment conditions. Application of the described method is illustrated through algorithm Na?ve Bayes.
收起
原文获取