Prediction in shopping cart uses partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy. In order to reduce the rule mining cost, a fast algorithm generating frequent itemsets without generating candidate itemsets is proposed. The algorithm uses Boolean vector with relational AND operation to discover frequent itemsets and generat...

The sequential pattern mining on progressive databases is comparatively very new, in which progressively find out the sequential patterns in time of interest. Time of interest is a sliding window which is continuously move forwards as the time goes by. As the focus of sliding window changes, the new items are added to the dataset of interest and obsolete items are removed from it and become up ...

The effect of practice on the operative form of a rule was investigated by giving subjects an easy, perfectly predictive classification rule, followed by training in applying that rule to a set of practice items. On a subsequent transfer test, the accuracy and speed of classifying new items was strongly affected by similarity to previously seen items, suggesting that the effect of practice was ...

TopCat (Topic Categories) is a technique for identifying topics that recur in articles in a text corpus. Natural language processing techniques are used to identify key entities in individual articles, allowing us to represent an article as a set of items. This allows us to view the problem in a database/data mining context: Identifying related groups of items. This paper presents a novel metho...

Frequent itemset mining in uncertain transaction databases semantically and computationally di ers from traditional techniques applied on standard (certain) transaction databases. Uncertain transaction databases consist of sets of existentially uncertain items. The uncertainty of items in transactions makes traditional techniques inapplicable. In this paper, we tackle the problem of nding proba...