Kritsada Sriphaew

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Generalized association rule mining is an extension of traditional association rule mining to discover more informative rules, given a taxonomy. In this paper, we describe a formal framework for the problem of mining generalized association rules. In the framework, The subset-superset and the parent-child relationships among generalized itemsets are(More)
SUMMARY Mining generalized frequent patterns of generalized association rules is an important process in knowledge discovery system. In this paper, we propose a new approach for efficiently mining all frequent patterns using a novel set enumeration algorithm with two types of constraints on two generalized itemset relationships, called subset-superset and(More)
Among a huge number of blogs on the internet, only some of them are considered to have great contents and worth to be explored. We call such kind of blogs cool blogs and attempt to identify them. To solve the cool blog identification problem, we consider three assumptions on cool blogs: (1) cool blogs tend to have definite topics, (2) cool blogs tend to(More)
With a large volume of electronic documents, finding documents the contents of which are same or similar in their topics has recently become a crucial aspect in textual data mining. Towards revealing so-called topic-based relationship among the documents, this paper proposes a method to exploit co-occurring unigrams and bi-grams among documents to extract a(More)
In the area of knowledge discovery in databases, the generalized association rule mining is an extension from the traditional association rule mining by given a database and taxonomy over the items in database. More initiative and informative knowledge can be discovered. In this work, we propose a novel approach of generalized closed itemsets. A smaller set(More)
SUMMARY Assessment of discovered patterns is an important issue in the field of knowledge discovery. This paper presents an evaluation method that utilizes citation (reference) information to assess the quality of discovered document relations. With the concept of transitivity as direct/indirect citations, a series of evaluation criteria is introduced to(More)
We address the problem of cool blog classification using only positive and unlabeled examples. We propose an algorithm, called PUB, that exploits the information of unlabeled data together with the positive examples to predict whether the unseen blogs are cool or not. The algorithm uses the weighting technique to assign a weight to each unla-beled example(More)
Food tour is popular and becomes one of the most dynamic and creative segments of tourism. Popular itinerary of food tour can be extracted from the information in the Internet, but preference of the user must also be taken into consideration. This paper proposed a modified Ant Colony algorithm to find best possible itineraries through approximation and(More)
The extension approach of frequent itemset mining can be applied to discover the relations among documents. Several schemes, i.e., n-gram, stemming, stopword removal and term weighting, can be applied to form different document representations for mining. It is necessary to formulate a benchmark for comparing the quality of discovered relations extracted(More)
Word-based relations among technical documents are immensely useful information but often hidden in a large amount of scientific publications. This work presents a method to apply latent semantic indexing in frequent itemset mining to discover potential relations among scientific publications. In this work, two weighting schemes, tf and tfidf are(More)
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