Characterizing Water Users through Frequent Patterns and Association Rules by Using Apriori Algorithm: A Case of Pangani Basin Tanzania

Abstract

Objectives: To identify the hidden patterns in the K-means clustered dataset for the Pangani Basin using the Apriori algorithm through frequent patterns and association rules to enrich cluster characteristics. Methods: Frequent patterns and association rule mining were used to discover the hidden attributes in the K-means clustered dataset. Measures of minimum support ranging from 0.5% to 5% and minimum confidence ranging from 50% to 100% were used to generate a manageable number of rules which were then filtered for redundancy. Lift value >1.0 was used to determine the rule’s interestingness while Arules and ArulesViz in R were used to visualize generated rules. Findings: Clusters one to four generated 25, 31, 47, and 49 rules respectively at a minimum confidence of 50% and minimum support of 2% in the first two clusters and 1% in other clusters. Furthermore, water users in cluster one were observed to abstract more water than the three clusters, while their water use fee also reflected on the amount they abstracted. In clusters two and three, water users identified the same amount of water source capacity but differed in the amount requested and water use fee. Water users in cluster four were identified with less water source capacity and fewer amounts abstracted than other clusters. However, their water use fee identified was higher than those in cluster three, with high water source capacity and high amount requested. Such a difference is attributed to the type of water use for cluster three users being domestically supplied through community water supply entities to help villagers access water. In contrast, the water use for users in cluster four is domestic and commercial. Novelty: When aggregated with the clustering observations, the identified association rules mining results provide a broad understanding of water users’ characteristics for better water allocation and rationing.