Changes in people's transaction behavior using the internet resulted exponential growth of e-commerce. With digital shopping transactions, it is difficult to predict customer segments and patterns traditional mathematical models. Timely identification emerging trends from large volumes data plays a major role business processes decision making. This different previous research works that apply ...

Mining association rules associates events that took place together. In market basket analysis, these discovered rules associate items purchased together. Items that are not part of a transaction are not considered. In other words, typical association rules do not take into account items that are part of the domain but that are not together part of a transaction. Association rules are based on ...

The development of good measures of interestingness of the discovered rules is one of the important problems in data mining. Such measures of interestingness are divided into objective measures : – those that depend only on the structure of a rule and the underlying data used in the discovery process, and the subjective measures – those that depend on the class of users who examine the rule. Ho...


 Petrokimia Gresik is a company engaged in the argo industry. In an effort to increase productivity, PT. has special port used for loading and unloading activities. During activities, equipment needed make it easier move cargo. Not infrequently this suffers damage that not known what object affects damage. Based on 2021 Asset Utilization Data, internal tools whose availability still below...

Inducing association rules is one of the central tasks in data mining applications. Quantitative association rules induced from databases describe rich and hidden relationships to be found within data that can prove useful for various application purposes (e.g., market basket analysis, customer profiling, and others). Although association rules are quite widely used in practice, a thorough anal...

In this paper, we use a new method to find the best predictor of software fault using association rule mining. The method, first of all select all the association rules having confidence greater than 40% and support greater than 30% using Apriori algorithm. After that our aim is to select top ‘n’ association rules out of a pool of ‘k’ association rules based on heuristic analysis. The method ra...

-Data mining includes number of techniques like clustering, classification, sequential patterns, association rules and etc. Association rule mining is a technique for mining interesting rules from large databases for further analysis. Association rule mining includes two-step approach for extracting the rules. These two steps require many database scan and use support and confidence as the thre...

Web usage mining aims to discover interesting user access patterns from web logs. Formal Concept Analysis (FCA) is an effective data analysis technique based on ordered lattice theory. In this paper, we propose a novel FCA approach for web usage mining. In our approach, the FCA technique is applied to mine association rules from Web Usage Lattice constructed from web logs. The discovered knowle...

Association rules present one of the most versatile techniques for the analysis of binary data, with applications in areas as diverse as retail, bioinformatics, and sociology. In this chapter, the origin of association rules is discussed along with the functions by which association rules are traditionally characterised. Following the formal definition of an association rule, these functions – ...