The mining of frequent patterns (or frequent itemsets) plays an essential role in many tasks of data mining. One major methodology for mining frequent patterns is the Apriori-based approach, which is computationally costly because many candidate itemsets have to be generated and verified. More recently, another approach using the Frequent-Pattern Tree (FP-tree) have been suggested to avoid the ...

For most manufacturers, success or failure is determined by how effectively and efficiently their products are sold through their marketing channel members, so the management of marketing channels plays an important role in market competition. Most existing work studies the problem of marketing channel management in a qualitative way. Recently, with the increase of amount of sales data, how to ...

Tree structures are used extensively in domains such as computational biology, pattern recognition, computer networks, and so on. In this paper, we present an indexing technique for free trees and apply this indexing technique to the problem of mining frequent subtrees. We first define a novel representation, the canonical form, for rooted trees and extend the definition to free trees. We also ...

Medically unexplained or functional somatic symptoms (FSS) in children constitute a major clinical problem. However, research data on FSS in young children are few, and epidemiological studies are hampered by lack of good standardised measures. The present thesis consists of two studies: In study one, we developed two measures to assess FSS in young children. The first measure is a parent i...

The mining of the complete set of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of frequent closed itemsets (FCIs), which results in a much smaller number of itemsets. The approaches to mining frequent closed itemsets can be categorized into two groups: those with candidate generation and those without. In this paper, we propose...

Mining frequent patterns in large transactional databases is a highly researched area in the field of data mining. Existing frequent pattern discovering algorithms suffer from many problems regarding the high memory dependency when mining large amount of data, computational and I/O cost. Additionally, the recursive mining process to mine these structures is also too voracious in memory resource...