A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to si...

Though metaheuristics have been frequently employed to improve the performance of data mining algorithms, the opposite is not true. This paper discusses the process of employing a data mining algorithm to improve the performance of a metaheuristic algorithm. The targeted algorithms to be hybridized are the Meta-heuristic for Randomized Priority Search (Meta-RaPS) and an algorithm used to create...

The research domain of process mining, or more specifically process discovery, aims at constructing a process model as an abstract representation of an event log. The goal is to build a model (i.e. in terms of a Petri net) that (1) can reproduce the log under consideration, and (2) does not allow for much more behaviour than shown in the log. The Theory of Regions can be used to transform a sta...

The amount of competition in the business world, especially sales industry, required developers to find a strategy that could increase and marketing products sold, one which was using clothing data with mining. Data Mining an iterative interactive process new patterns or models can be generalized for future, valuable, understandable massive database. HAS Stores arrangement goods layout still pl...

Sequential pattern mining is a fundamental and essential field of data mining because of its extensive scope of applications spanning from the forecasting the user shopping patterns, and scientific discoveries. The objective is to discover frequently appeared sequential patterns in given set of sequences. Now-a-days, many studies have contributed to the efficiency of sequential pattern mining a...

Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information regarding a business issue. Driven by this methodology, domain intelligence is not necessary in targeting the demonstration of an algorithm. As a result, very often knowledge discovered is not generally interesting to business needs. However...

Swarm Intelligence (SI) is an innovative artificial intelligence technique for solving complex optimization problems. Data clustering is the process of grouping data into a number of clusters. The goal of data clustering is to make the data in the same cluster share a high degree of similarity while being very dissimilar to data from other clusters. Clustering algorithms have been applied to a ...

Swarm Intelligence (SI) is an innovative artificial intelligence technique for solving complex optimization problems. Data clustering is the process of grouping data into a number of clusters. The goal of data clustering is to make the data in the same cluster share a high degree of similarity while being very dissimilar to data from other clusters. Clustering algorithms have been applied to a ...

Web Usage Mining discovers interesting patterns in accesses to various Web pages within the Web space associated with a particular server. The Web Usage Mining architecture divides the process into two main partsthe first part includes preprocessing, transaction identification, and data integration components. The second part includes the largely domain independent application of generic data m...

Web Usage Mining discovers interesting patterns in accesses to various Web pages within the Web space associated with a particular server. The Web Usage Mining architecture divides the process into two main partsthe first part includes preprocessing, transaction identification, and data integration components. The second part includes the largely domain independent application of generic data m...