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