53,406 research outputs found
Re-mining item associations: methodology and a case study in apparel retailing
Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques
A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm
As the growing interest of web recommendation systems those are applied to
deliver customized data for their users, we started working on this system.
Generally the recommendation systems are divided into two major categories such
as collaborative recommendation system and content based recommendation system.
In case of collaborative recommen-dation systems, these try to seek out users
who share same tastes that of given user as well as recommends the websites
according to the liking given user. Whereas the content based recommendation
systems tries to recommend web sites similar to those web sites the user has
liked. In the recent research we found that the efficient technique based on
asso-ciation rule mining algorithm is proposed in order to solve the problem of
web page recommendation. Major problem of the same is that the web pages are
given equal importance. Here the importance of pages changes according to the
fre-quency of visiting the web page as well as amount of time user spends on
that page. Also recommendation of newly added web pages or the pages those are
not yet visited by users are not included in the recommendation set. To
over-come this problem, we have used the web usage log in the adaptive
association rule based web mining where the asso-ciation rules were applied to
personalization. This algorithm was purely based on the Apriori data mining
algorithm in order to generate the association rules. However this method also
suffers from some unavoidable drawbacks. In this paper we are presenting and
investigating the new approach based on weighted Association Rule Mining
Algorithm and text mining. This is improved algorithm which adds semantic
knowledge to the results, has more efficiency and hence gives better quality
and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table
The Bases of Association Rules of High Confidence
We develop a new approach for distributed computing of the association rules
of high confidence in a binary table. It is derived from the D-basis algorithm
in K. Adaricheva and J.B. Nation (TCS 2017), which is performed on multiple
sub-tables of a table given by removing several rows at a time. The set of
rules is then aggregated using the same approach as the D-basis is retrieved
from a larger set of implications. This allows to obtain a basis of association
rules of high confidence, which can be used for ranking all attributes of the
table with respect to a given fixed attribute using the relevance parameter
introduced in K. Adaricheva et al. (Proceedings of ICFCA-2015). This paper
focuses on the technical implementation of the new algorithm. Some testing
results are performed on transaction data and medical data.Comment: Presented at DTMN, Sydney, Australia, July 28, 201
Multiple Hypothesis Testing in Pattern Discovery
The problem of multiple hypothesis testing arises when there are more than
one hypothesis to be tested simultaneously for statistical significance. This
is a very common situation in many data mining applications. For instance,
assessing simultaneously the significance of all frequent itemsets of a single
dataset entails a host of hypothesis, one for each itemset. A multiple
hypothesis testing method is needed to control the number of false positives
(Type I error). Our contribution in this paper is to extend the multiple
hypothesis framework to be used with a generic data mining algorithm. We
provide a method that provably controls the family-wise error rate (FWER, the
probability of at least one false positive) in the strong sense. We evaluate
the performance of our solution on both real and generated data. The results
show that our method controls the FWER while maintaining the power of the test.Comment: 28 page
- …