16,995 research outputs found
New approaches to weighted frequent pattern mining
Researchers have proposed frequent pattern mining algorithms that are more
efficient than previous algorithms and generate fewer but more important patterns. Many
techniques such as depth first/breadth first search, use of tree/other data structures, top
down/bottom up traversal and vertical/horizontal formats for frequent pattern mining
have been developed. Most frequent pattern mining algorithms use a support measure to
prune the combinatorial search space. However, support-based pruning is not enough
when taking into consideration the characteristics of real datasets. Additionally, after
mining datasets to obtain the frequent patterns, there is no way to adjust the number of
frequent patterns through user feedback, except for changing the minimum support.
Alternative measures for mining frequent patterns have been suggested to address these
issues. One of the main limitations of the traditional approach for mining frequent
patterns is that all items are treated uniformly when, in reality, items have different
importance. For this reason, weighted frequent pattern mining algorithms have been
suggested that give different weights to items according to their significance. The main
focus in weighted frequent pattern mining concerns satisfying the downward closure
property. In this research, frequent pattern mining approaches with weight constraints are
suggested. Our main approach is to push weight constraints into the pattern growth
algorithm while maintaining the downward closure property. We develop WFIM
(Weighted Frequent Itemset Mining with a weight range and a minimum weight),
WLPMiner (Weighted frequent Pattern Mining with length decreasing constraints), WIP
(Weighted Interesting Pattern mining with a strong weight and/or support affinity),
WSpan (Weighted Sequential pattern mining with a weight range and a minimum
weight) and WIS (Weighted Interesting Sequential pattern mining with a similar level of
support and/or weight affinity)
The extensive performance analysis shows that suggested approaches are
efficient and scalable in weighted frequent pattern mining
Prediction of peptides binding to MHC class I alleles by partial periodic pattern mining
MHC (Major Histocompatibility Complex) is a key player in the immune response of an organism. It is important to be able to predict which antigenic peptides will bind to a spe-cific MHC allele and which will not, creating possibilities for controlling immune response and for the applications of immunotherapy. However a problem encountered in the computational binding prediction methods for MHC class I is the presence of bulges and loops in the peptides, changing the total length. Most machine learning methods in use to-day require the sequences to be of same length to success-fully mine the binding motifs. We propose the use of time-based data mining methods in motif mining to be able to mine motifs position-independently. Also, the information for both binding and non-binding peptides are used on the contrary to the other methods which only rely on binding peptides. The prediction results are between 70-80% for the tested alleles
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Data Mining for Shopping Centres – Customer Knowledge-Management Framework
Shopping centers are an important part of the UK economy and have been the subject of considerable research. Relying on complex interdependencies between shoppers, retailers and owners, shopping centers are ideal for knowledge management study. Nevertheless, although retailers have been in the forefront of data mining, little has been written on Customer Knowledge Management for shopping centers. In this chapter, the authors aim to demonstrate the possibilities and draw attention to the possible implications of improving customer satisfaction. Aspects of customer knowledge management for shopping centers are considered using analogies drawn from an exploratory questionnaire survey. The objectives of a Customer Knowledge Management system could include increasing rental incomes and bringing new life back into shopping centers and towns
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