Search

Frequent and infrequent itemset mining are trending in data mining techniques. The pattern of Association Rule (AR) generated will help decision maker or business policy maker to project for the next intended items across a wide variety of applications. While frequent itemsets are dealing with items that are most purchased or used, infrequent items are those items that are infrequently occur or also called rare items. The AR mining still remains as one of the most prominent areas in data mining that aims to extract interesting correlations, patterns, association or casual structures among set of items in the transaction databases or other data repositories. The design of database structure in association rules mining algorithms are based upon horizontal or vertical data formats. These two data formats have been widely discussed by showing few examples of algorithm of each data formats. The efforts on horizontal format suffers in huge candidate generation and multiple database scans which resulting in higher memory consumptions. To overcome the issue, the solutions on vertical approaches are proposed. One of the established algorithms in vertical data format is Eclat.ECLAT or Equivalence Class Transformation algorithm is one example solution that lies in vertical database format. Because of its ‘fast intersection’, in this paper, we analyze the fundamental Eclat and Eclatvariants such asdiffsetand sortdiffset. In response to vertical data format and as a continuity to Eclat extension, we propose a postdiffset algorithm as a new member in Eclat variants that use tidset format in the first looping and diffset in the later looping. In this paper, we present the performance of Postdiffset algorithm prior to implementation in mining of infrequent or rare itemset. Postdiffset algorithm outperforms 23% and 84% to diffset and sortdiffset in mushroom and 94% and 99% to diffset and sortdiffset in retail dataset. [ABSTRACT FROM AUTHOR]

University or Higher Learning Institution is a platform to train students in specific domain area that will become an asset for a country. One of the critical issues in University is to avoid dropout students. Educational Data Mining (EDM) is an emergent discipline in developing methods to explore the unique types of data from the educational context. One of the techniques applied in EDM is an Association Rules (AR) which is to find a pattern of one element influencing other element. In this paper, AR technique has been utilized to predict students' results based on a group of the previous students results. Clustering method has been used to classify the specific subject category. Finally, a system known as Result Prediction System (REPS) has been developed to perform automatically the AR analysis on the academic data using PHP and MYSQL databases. [ABSTRACT FROM AUTHOR]

Studies on the relationship between knowledge and attitude (KA) and cognitive status of drug misuders are generally still limited in Malaysia. In particular, those who are seeking religious-based modalities to treat addiction problems. This study aimed to 1) determine the cognitive status and KA among drug addicts and 2) compare the KA of drug addicts with different cognitive status (regrouped into "Good" and "Poor"). Drug misusers who were undergoing the Islamic (Inabah) Therapy Module were enrolled from Pusat Rawatan Darul Taubah in Pasir Puteh, Kelantan. The Malay Sahlgrenska Academy Self - Reported Cognitive Impairment Questionnaire (SASCI - Q) and Drug - Related Knowledge, Attitudes and Belief (KA) Questionnaire were administered (higher scores indicating favourable cognitive and KA status). Data was analysed using SPSS 23, employing descriptive and non-parametric techniques. Thirty - seven male Muslim participants were recruited (age = 28.4 ± 7.0 years; single = 81.1%; PMR/SRP/LCE qualification = 45.9%; self-employed = 62.2%). Both cognitive status and KA were moderate, with mean of 66.2 ± 13.3 and 57.3 ± 12.5 respectively. Those who started addiction younger had worse cognitive status. With regard to KA, no significant difference was detected between respondents possessing different cognitive status (all p > 0.05). Nonetheless, the general trend demonstrated that those with higher cognitive status (median ≥ 66.2) reported relatively better knowledge (60.4 ± 16.9). More favourable knowledge and attitudes profiles were similarly exhibited by the group with higher cognitive status. [ABSTRACT FROM AUTHOR]