Mining High Utility Itemsets from a transaction database is to find itemsests that have utility above a user-specified threshold. This problem is an extension of Frequent Itemset ...
One of the inherent problems in pattern recognition is the undersampled data problem, also known as the curse of dimensionality reduction. In this paper a new algorithm called pai...
The maintenance dataset provided by SunWater contains information about failed assets also known as components and their corresponding failure modes. Currently, extraction of this...
The detection of unusual or anomalous data is an important function in automated data analysis or data mining. However, the diversity of anomaly detection algorithms shows that it...
When an agent enters in an e-Market for the first time, it has no historical information that can be used to determine the strength of business relationship with participant agen...
Mining frequent patterns such as frequent itemsets is a core operation in many important data mining tasks, such as in association rule mining. Mining frequent itemsets in high-di...
Clustering time series data using the popular subsequence (STS) technique has been widely used in the data mining and wider communities. Recently the conclusion was made that it i...
Educators developing data mining courses face a difficult task of designing curricula that are adaptable, have solid foundations, and are tailored to students from different acade...
This article explicitly outlines an approach designed to allow optimal utilisation of Analytics in the industry setting. The paper focuses on the key stages of the Analytics proce...