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.

Impact of cannabis use on long-term remission in bipolar I and schizoaffective disorder

OBJECTIVE: To investigate the impact of regular cannabis use on long-term remission of mood symptoms in bipolar spectrum disorders. METHODS: The 24-month prospective observational study included patients (n=239) with bipolar I disorder and schizoaffective disorder, bipolar type. Participants were classified as regular cannabis users (three times or more per week) or non-users. The primary outcome measure was the achievement of remission on the evaluations during the 24 months. RESULTS: Of the 234 participants for whom data was available, 25 (10.7%) were regular cannabis users, and the group comprised significantly more males than females. In the total population, cannabis use was significantly associated with decreased likelihood of remission during the 24-month follow-up period. Subgroup analyses showed that cannabis use was significantly associated with lower remission rates on the Hamilton Depression Rating Scale in females (n=139) and patients prescribed mood stabilizers alone (n=151), whereas in males (n=95) and patients prescribed olanzapine and/or a mood stabilizer (n=83), cannabis use was significantly associated with lower remission rates on the Young Mania Rating Scale. Remission rates were lowest in the concurrent cannabis and tobacco smoking group (n=22) followed by the tobacco smoking only group (n=97), and the non-smoker group (n=116). The post-hoc analysis revealed that all remission rates were significantly lower in the concurrent cannabis and the tobacco smoking group compared to the non-smoker group. CONCLUSION: Cannabis use negatively affects the long-term clinical outcome in patients with bipolar spectrum disorders. A comprehensive assessment and integrated management of cannabis use are required to achieve better treatment outcomes for bipolar spectrum disorders.