A Grammar-Guided Genetic Programing Algorithm for Associative Classification in Big Data

2019 ◽  
Vol 11 (3) ◽  
pp. 331-346 ◽  
Author(s):  
F. Padillo ◽  
J. M. Luna ◽  
S. Ventura
Keyword(s):  
Big Data ◽  
Associative Classification

scholarly journals A MapReduce solution for associative classification of big data

2016 ◽  
Vol 332 ◽  
pp. 33-55 ◽  
Author(s):  
Alessio Bechini ◽  
Francesco Marcelloni ◽  
Armando Segatori
Keyword(s):  
Big Data ◽  
Associative Classification

scholarly journals Reliable Distributed Fuzzy Discretizer for Associative Classification of Big data

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Associative Classification ◽  
Data Set ◽  
Digital World ◽  
Huge Data ◽  
Fuzzy Partitioning ◽  
Discrete Values ◽  
Associative Classifiers

Data Mining is an essential task because the digital world creates huge data daily. Associative classification is one of the data mining task which is used to carry out classification of data, based on the demand of knowledge users. Most of the associative classification algorithms are not able to analyze the big data which are mostly continuous in nature. This leads to the interest of analyzing the existing discretization algorithms which converts continuous data into discrete values and the development of novel discretizer Reliable Distributed Fuzzy Discretizer for big data set. Many discretizers suffer the problem of over splitting the partitions. Our proposed method is implemented in distributed fuzzy environment and aims to avoid over splitting of partitions by introducing a novel stopping criteria. Proposed discretization method is compared with existing distributed fuzzy partitioning method and achieved good accuracy in the performance of associative classifiers.

scholarly journals Rough Sets Base Associative Classification Rules Extraction from Big Data

2019 ◽  
Vol 9 (1) ◽  
pp. 3096-3102
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Extraction Process ◽  
Classification Rules ◽  
Associative Classification ◽  
Mapreduce Framework ◽  
Data Set ◽  
Suggested Approach ◽  
Standard Data ◽  
Rules Extraction

Big Data is a current burning challenge for the data analytics research community. Many conventional data analytics techniques have been extended to the MapReduce framework to process Big Data. But in our literature review, we find that for the MapReduce system there is an absolute lack of rough setbased technique. To facilitate this and recognize the importance of the rule-based classification techniques, we suggest a roughset associative classification rules extraction process for the MapReduce framework. The implementation and evaluation of the Big Data Standard data set demonstrated the efficiency of our suggested approach.

scholarly journals Evaluating associative classification algorithms for Big Data

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Francisco Padillo ◽  
José María Luna ◽  
Sebastián Ventura
Keyword(s):  
Big Data ◽  
Classification Algorithms ◽  
Associative Classification

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

“Big Data in Psychology: Methods and Applications”

2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  
Big Data

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org

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