Abstract
ABSTRACTThe 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.
Highlights
There is a significant amount of data stored in the databases, and with the rapid spread of the data warehouse, it is necessary to find techniques to extract information and knowledge by exploiting these data stored for used in problem-solving and decision-making using modern computer applications, the current smart technology famous as artificial intelligence
Data mining and machine learning are topics in artificial intelligence that focus on pattern discovery, prediction, and forecasting based on possessions of gathered data (Witten, Frank, Hall, & Pal, 2016)
This paper showed that when applying rule covers method on the generated class association rules using Apriori and Predictive Apriori algorithms, many class association rules produced by Apriori algorithm were eliminated, and more effective rules than those generated by Predictive Apriori algorithm were obtained
Summary
There is a significant amount of data stored in the databases, and with the rapid spread of the data warehouse, it is necessary to find techniques to extract information and knowledge by exploiting these data stored for used in problem-solving and decision-making using modern computer applications, the current smart technology famous as artificial intelligence. Data mining is an analytical process that combines artificial intelligence, statistics, and machine learning. It is considered a step of knowledge in databases. Data mining and machine learning are topics in artificial intelligence that focus on pattern discovery, prediction, and forecasting based on possessions of gathered data (Witten, Frank, Hall, & Pal, 2016).
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