Numerous research studies have explored the effect of hypermedia on learners’ performance using Web Based Instruction (WBI). A learner’s performance is determined by their varying skills and abilities as well as various differences such as gender, cognitive style and prior knowledge. In this paper, we investigate how differences between individuals influenced learner’s performance using a hyper...

The immense capacity of web usage data which survives on web servers contains potentially precious information about the performance of website visitors. Pattern Mining involves applying data mining methods to large web data repositories to extract usage patterns. Due to the emerging reputation of the World Wide Web, many websites classically experience thousands of visitors every day. Examinat...

This paper applies the data mining techniques in the area of data cleaning as effective in discovering Constant Conditional Functional Dependencies(CCFDs) from relational databases . These CCFDs are used as business rules for context dependent data validations. Conditional Functional Dependencies(CFDs) are an extension of Functional dependencies(FDs) which captures the consistency of data by su...

The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this article, we present an algorithm that provides outlier detection and data clustering simul...

This article examines several data mining approaches that perform short time series analysis. The basis of the methods is formed by clustering algorithms with or without modifications. The proposed methods implement short time series analysis when the numbers of the observations are not equal and the historical information is short. The inspected approaches are offered for solving complex tasks...

Data mining on huge databases has been a major issue in research area, due to the problem of analyzing large volumes of data using traditional OLAP tools only. This type of process implies much computational power, disk I/O and memory, which can be used only by parallel computers. So, depending on the selection of the parameters (the minimum support and minimum confidence), current algorithms c...

One of the predominant techniques used in the area of data mining is association rule mining. In real world, data mining analysts usually are confronted with a dynamic environment; the database would be changed over time, and the analysts may need to set different support constraints to discover real informative rules. Efficiently updating the discovered association rules thus becomes a crucial...

Mining the Data is also known as Discovery of Knowledge in Databases. It is to get correlations, trends, patterns, anomalies from the databases which can help to build exact future decisions. However data mining is not the natural. No one can assure that the decision will lead to good quality results. It only helps experts to understand the data and show the way to good decisions. Association M...

  The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the ...