Data and knowledge mining with big data towards smart production

2018 ◽  
Vol 9 ◽  
pp. 1-13 ◽  
Author(s):  
Ying Cheng ◽  
Ken Chen ◽  
Hemeng Sun ◽  
Yongping Zhang ◽  
Fei Tao
Keyword(s):  
Big Data ◽  
Knowledge Mining

scholarly journals Analysis study on R-Eclat algorithm in infrequent itemsets mining

2019 ◽  
Vol 9 (6) ◽  
pp. 5446
Author(s):  
Mustafa Man ◽  
Julaily Aida Jusoh ◽  
Syarilla Iryani Ahmad Saany ◽  
Wan Aezwani Wan Abu Bakar ◽  
Mohd Hafizuddin Ibrahim
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Sparse Data ◽  
Experimental Results ◽  
Frequency Of Occurrence ◽  
Knowledge Mining ◽  
Analysis Study ◽  
Itemset Mining ◽  
Support Measure ◽  
Data Environment

There are rising interests in developing techniques for data mining. One of the important subfield in data mining is itemset mining, which consists of discovering appealing and useful patterns in transaction databases. In a big data environment, the problem of mining infrequent itemsets becomes more complicated when dealing with a huge dataset. Infrequent itemsets mining may provide valuable information in the knowledge mining process. The current basic algorithms that widely implemented in infrequent itemset mining are derived from Apriori and FP-Growth. The use of Eclat-based in infrequent itemset mining has not yet been extensively exploited. This paper addresses the discovery of infrequent itemsets mining from the transactional database based on Eclat algorithm. To address this issue, the minimum support measure is defined as a weighted frequency of occurrence of an itemsets in the analysed data. Preliminary experimental results illustrate that Eclat-based algorithm is more efficient in mining dense data as compared to sparse data.

scholarly journals Big Data Knowledge Mining

2016 ◽  
Vol 7 (11) ◽  
Author(s):  
Huda Umar ◽  
Fathy Eassa ◽  
Kamal Jambi ◽  
Maysoon Abulkhair
Keyword(s):  
Big Data ◽  
Knowledge Mining

VIM: A Big Data Analytics Tool for Data Visualization and Knowledge Mining

2017 ◽  
Author(s):  
Sk. Shariful Islam Arafat ◽  
Md Shakil Hossain ◽  
Md. Mahmudul Hasan ◽  
S M Al-Hossain Imam ◽  
Md. Mofijul Islam ◽  
...  
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Knowledge Mining

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.

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