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...
Frequent subtree mining has attracted a great deal of interest among the researchers due to its application in a wide variety of domains. Some of the domains include bio informati...
To date, most association rule mining algorithms have assumed that the domains of items are either discrete or, in a limited number of cases, hierarchical, categorical or linear. ...
This paper describes the development of a predictive model for corporate insolvency risk in Australia. The model building methodology is empirical with out-ofsample future year te...
The efficient market hypothesis states that the market incorporates all available information to provide an accurate valuation of the asset at any given time. However, most models...