143,483 research outputs found
Issues and Techniques of Spatio -Temporal Rule Mining for Location Based Services
The Convergence of location-aware devices, wireless communication, such as the increasing accuracy of GPS technology and geographic information system functionalities enables the deployment of new services such as location-based services (LBS). Achieve high quality or such services, spatio2013;temporal data mining techniques are needed. Our work concentrates on the development of data mining techniques for knowledge discovery and delivery in LBS. First, a number of real world spatio2013;temporal data sets are described, leading to a taxonomy of spatio2013;temporal data. Second, the paper describes a general methodology that transforms the spatio2013;temporal rule mining task to the traditional market basket analysis task and applies it to the described data sets, enabling traditional association rule mining methods to discover spatio2013;temporal rules for LBS. Finally, unique issues in spatio2013;temporal rule mining are identified and discussed
Mining Event Logs to Support Workflow Resource Allocation
Workflow technology is widely used to facilitate the business process in
enterprise information systems (EIS), and it has the potential to reduce design
time, enhance product quality and decrease product cost. However, significant
limitations still exist: as an important task in the context of workflow, many
present resource allocation operations are still performed manually, which are
time-consuming. This paper presents a data mining approach to address the
resource allocation problem (RAP) and improve the productivity of workflow
resource management. Specifically, an Apriori-like algorithm is used to find
the frequent patterns from the event log, and association rules are generated
according to predefined resource allocation constraints. Subsequently, a
correlation measure named lift is utilized to annotate the negatively
correlated resource allocation rules for resource reservation. Finally, the
rules are ranked using the confidence measures as resource allocation rules.
Comparative experiments are performed using C4.5, SVM, ID3, Na\"ive Bayes and
the presented approach, and the results show that the presented approach is
effective in both accuracy and candidate resource recommendations.Comment: T. Liu et al., Mining event logs to support workflow resource
allocation, Knowl. Based Syst. (2012), http://dx.doi.org/
10.1016/j.knosys.2012.05.01
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
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