2,483 research outputs found
Evaluation and optimization of frequent association rule based classification
Deriving useful and interesting rules from a data mining system is an essential and important task. Problems
such as the discovery of random and coincidental patterns or patterns with no significant values, and the
generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness
of rules generated by data mining algorithms are actively and constantly being examined and developed. In this
paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms,
combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task
Non-redundant rare itemset generation
Rare itemsets are likely to be of great interest because they often relate to high-impact transactions which may give rise to rules of great practical signi cance. Research into the rare association rule mining problem has gained momentum in the recent past. In this paper, we propose a novel approach that captures such rare rules while ensuring that redundant rules are eliminated. Extensive testing on real-world datasets from the UCI repository con rm that our approach outperforms both the Apriori-Inverse(Koh et al. 2006) and Relative Support (Yun et al. 2003) algorithms
Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data
Ambient intelligence (AmI) provides adaptive, personalized, intelligent, ubiquitous and interactive services to wide range of users. AmI can have a variety of applications, including smart shops, health care, smart home, assisted living, and location-based services. Tourist guidance is one of the applications where AmI can have a great contribution to the quality of the service, as the tourists, who may not be very familiar with the visiting site, need a location-aware, ubiquitous, personalised and informative service. Such services should be able to understand the preferences of the users without requiring the users to specify them, predict their interests, and provide relevant and tailored services in the most appropriate way, including audio, visual, and haptic. This paper shows the use of crowd sourced trajectory data in the detection of points of interests and providing ambient tourist guidance based on the patterns recognised over such data
Analyzing association rules produced by applying the apriori algorithm to structured data
In this thesis, we will use various techniques from data mining to draw interesting results from a set of structured data on personal privacy information. In particular, the well-known, Apriori Algorithm will be used to find frequent item sets and association rules in this data. This process has been shown to be effective in predicting the presence of one type of data when other data is present in other data mining applications; The thesis will also include a detailed analysis of rules generated by the algorithm and their natural interpretations
Sequential Event Prediction with Association Rules
We consider a supervised learning problem in which data are revealed sequentially and the
goal is to determine what will next be revealed. In the context of this problem, algorithms
based on association rules have a distinct advantage over classical statistical and machine
learning methods; however, there has not previously been a theoretical foundation established for using association rules in supervised learning. We present two simple algorithms
that incorporate association rules, and provide generalization guarantees on these algorithms based on algorithmic stability analysis from statistical learning theory. We include
a discussion of the strict minimum support threshold often used in association rule mining,
and introduce an "adjusted confidence" measure that provides a weaker minimum support
condition that has advantages over the strict minimum support. The paper brings together
ideas from statistical learning theory, association rule mining and Bayesian analysis
Mining Fuzzy Coherent Rules from Quantitative Transactions Without Minimum Support Threshold
[[abstract]]Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should be predefined, and it is hard to set the appropriate one, and (2) the derived rules usually expose common-sense knowledge which may not be interested in business point of view. In this paper, we thus proposed an algorithm for mining fuzzy coherent rules to overcome those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy four criteria or not. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.[[incitationindex]]EI[[conferencetype]]ĺś‹éš›[[conferencedate]]20120610~20120615[[iscallforpapers]]Y[[conferencelocation]]Brisbane, Australi
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