Mining allocating patterns in one-sum weighted items

An Association Rule (AR) is a common knowledge model in data mining that describes an implicative cooccurring relationship between two disjoint sets of binary-valued transaction database attributes (items), expressed in the form of an "antecedent⇒ consequent" rule. A variant of the AR is the Weighted Association Rule (WAR). With regard to a marketing context, this paper introduces a new knowledge model in data mining -ALlocating Pattern (ALP). An ALP is a special form of WAR, where each rule item is associated with a weighting score between 0 and 1, and the sum of all rule item scores is 1. It can not only indicate the implicative co-occurring relationship between two (disjoint) sets of items in a weighted setting, but also inform the "allocating" relationship among rule items. ALPs can be demonstrated to be applicable in marketing and possibly a surprising variety of other areas. We further propose an Apriori based algorithm to extract hidden and interesting ALPs from a "one-sum" weighted transaction database. The experimental results show the effectiveness of the proposed algorithm. © 2008 IEEE.

Designing blended spaces to maximise student learning in work integrated learning programs

This paper describes a case study at a large metropolitan university in Australia where a range of technology-enabled blended spaces are used for interaction, communication and reflection between the work and university environments to enrich students' learning experiences during their work placement year. Blended space design requirements to maximise the learning experience of students undertaking work integrated learning are identified. © 2009 Friederika Kaider, Kathy Henschke, Joan Richardson and Mary Paulette Kelly.