Usage Apriori and clustering algorithms in WEKA tools to mining dataset of traffic accidents

ABSTRACT

The aim of this study is finding approaches for investigating association rules mining algorithms and clustering to offer new rules from a broad set of discovered rules which taken from traffic accident data at Alghat Provence in KSA. Several tools are applying in data mining to extracting data. WEKA provides applications of learning algorithms that can efficiently execute any dataset. In WEKA tools, there are many algorithms used to mining data. Apriori and cluster are the first-rate and most famed algorithms. Apriori is the simple algorithm, which applied for mining of repeated the patterns from the transaction dataset to find frequent itemsets and association between various item sets. A cluster is a technique used to group a collection of items having similar features. Association rules applied to find the connection between data items in a transactional database. Association rules data mining algorithms used to discover frequent association. WEKA tools were used to analysing traffic dataset, which composed of 946 instances and 8 attributes. Apriori algorithm and EM cluster were implemented for traffic dataset to discover the factors, which causes accidents. Through the results, shows that the Apriori algorithm is better than the EM cluster algorithm.

KEYWORDS:

• Data mining
• association rules
• clustering
• traffic accidents
• Apriori
• EM algorithm

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Faisal Mohammed Nafie Ali received the B.Sc. from Omdurman Ahlia University, Faculty of Applied & Computer science in Sudan, in 2001. He got M.Sc. and Ph.D. degree in Computer Science in 2009 and 2014 respectively from Alneelain University Faculty of Computer science and Information Technology in Sudan. He worked in the field of education as Computer Science teacher in Sudan from 2002 to 2013. He worked as Assistant Professor in Majmaah University, Suadia Arabia from 2014 until now; He worked as Oracle Database Administrator in National Pensions Fund in Sudan from 2007 to 2014. He has an experience in Data mining using WEKA and Clementine. He has many Certifications in oracle database Administrator.

Abdelmoneim Ali Mohamed Hamed received the B.Sc. and M.Sc.in mathematics in Sudan, Alnileen University Faculty of science, in 1989 and 2005 respectively. He has received Ph.D. in applied statistics in Sudan, Sudan University for science and technology, 2012. From 1989 to 2008, He worked in the field of education as a mathematics teacher in Sudan and Saudi Arabia. From 2009 to 2013, He worked as a lecturer at Al Ahfad University for Girls. From 2014 until now, he worked as Assistant Professor at Al Majmaah University. He has an experience in statistical analysis using SPSS and WEKA.