Hot Utility

Business intelligence (BI) is formulation of business strategies which help organizations to achieve its objectives and to predict its future. Data mining is often referred as BI in the domain of business. One of the major tasks in data mining is Association Rule Mining (ARM). ARM techniques incorporated in BI systems can be utilized in business decision-making such as retail shelf management, catalog design, customer segmentation, cross-selling, quality improvement and bundling products marketing. ARM technique is used for the identification of frequent itemsets from huge databases and then generating strong association rules by considering each item having same value. But in a large number of real world applications, items have different values according to their impact on the respective decision making processes. Traditional ARM techniques cannot fulfil the arising demands from these applications. The data mining researchers are continuously improving the quality of ARM technique by incorporating the utility of items. The utility of item is decided by its contribution towards the business profit or quantity of the item sold, etc. Hence Utility mining focuses on identifying the itemsets with high utilities. Jyothi et al proposed HURI algorithm in [2] for producing high utility rare itemset according to users' interest. An algorithm Transaction Profitability using HURI [TPHURI] is proposed in this paper which is a modified version of HURI. TPHURI finds profitable transactions consisting of high utility rare items and also finds the share of such items in the overall profit of the transactions.

Business intelligence (BI) is formulation of business strategies which help organizations to achieve its objectives and to predict its future. Data mining is often referred as BI in the domain of business. One of the major tasks in data mining is Association Rule Mining (ARM). ARM techniques incorporated in BI systems can be utilized in business decision-making such as retail shelf management, catalog design, customer segmentation, cross-selling, quality improvement and bundling products marketing. ARM technique is used for the identification of frequent itemsets from huge databases and then generating strong association rules by considering each item having same value. But in a large number of real world applications, items have different values according to their impact on the respective decision making processes. Traditional ARM techniques cannot fulfil the arising demands from these applications. The data mining researchers are continuously improving the quality of ARM technique...

In this article, the capability of discrete wavelet transform (DWT) to discriminate tree species with different ages using airborne hyperspectral remote sensing is investigated. The performance of DWT is compared against commonly used traditional methods, i.e. original reflectance and first and second derivatives. The hyperspectral data are obtained from Thetford forest of the UK, which contains Corsican and Scots pines with different ages and broadleaved tree species. The discrimination is performed by employing three different spectral measurement techniques (SMTs) including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and a combination of SAM and SID. Five different mother wavelets with a total of 50 different orders are tested. The wavelet detail coefficient (CD) from each decomposition level and combination of all CDs plus the approximation coefficient from the final decomposition level (C-All) are extracted from each mother wavelet. The results show the superiority of DWT against the reflectance and derivatives for all the three SMTs. In DWT, C-All provided the highest discrimination accuracy compared to other coefficients. An overall accuracy difference of about 20-30% is observed between the finest coefficient and C-All. Amongst the SMTs, SID provided the highest accuracy, while SAM showed the lowest accuracy. Using DWT in combination with SID, an overall accuracy up to around 71.4% is obtained, which is around 13.5%, 14.7%, and 27% higher than the accuracies achieved with reflectance and first and second derivatives, respectively.