An Efficient Technique for mining Association rules using Enhanced Apriori Algorithm

There are Various mining algorithms of association rules. One of the most popular algorithm is Apriori that extracts frequent itemset from large database and getting the association rule for discovering the knowledge. this paper pitfalls the limitation of the original Apriori algorithm for wasting time for scanning the whole database searching on to the frequent itemsets, and presents an technique on Apriori by reducing that wasted time depending on scanning only some transactions whose support value is bigger than 25% of minimum Support is taken as frequent item set and is added to the frequent item sets and then rules are formed. An enhanced Apriori algorithm may find the tendency of a customer on the basis of frequently purchased item-sets The proposed algorithm is useful as a frequent item sets predictor with lower number of scans. DOI: 10.17762/ijritcc2321-8169.15063

Studying patterns of use of transport modes through data mining - Application to U.S. national household travel survey data set

Data collection activities related to travel require large amounts of financial and human resources to be conducted successfully. When available resources are scarce, the information hidden in these data sets needs to be exploited, both to increase their added value and to gain support among decision makers not to discontinue such efforts. This study assessed the use of a data mining technique, association analysis, to understand better the patterns of mode use from the 2009 U.S. National Household Travel Survey. Only variables related to self-reported levels of use of the different transportation means are considered, along with those useful to the socioeconomic characterization of the respondents. Association rules potentially showed a substitution effect between cars and public transportation, in economic terms but such an effect was not observed between public transportation and nonmotorized modes (e.g., bicycling and walking). This effect was a policy-relevant finding, because transit marketing should be targeted to car drivers rather than to bikers or walkers for real improvement in the environmental performance of any transportation system. Given the competitive advantage of private modes extensively discussed in the literature, modal diversion from car to transit is seldom observed in practice. However, after such a factor was controlled, the results suggest that modal diversion should mainly occur from cars to transit rather than from nonmotorized modes to transi

Opportunities of Trade in Services between the EU and Ukraine: the Case of Telecommunications Services under the GATS and the Association Agreement

This working paper studies the legal and regulatory conditions for trade in services between the European Union (EU) and Ukraine on the example of tele-communications services that are important carrier services for various business activities in the cross-border trade. The paper outlines the general frame-work for trade in services under the GATS as expressed in the commitments undertaken by Ukraine and examines the detailed provisions of the EU-Ukraine Association Agreement on trade liberalisation and regulatory approximation that is a WTO-extra agreement. It also provides an overview of the relevant Ukrainian legal and regulatory rules in order to assess the starting point for regulatory approximation. The paper intends to highlight internal contradictions of the Association Agreement representing a balancing act be-tween the liberalisation obligations under the WTO agreements and requirements of integration in the EU internal market. The paper argues that in short to middle term the Association Agreement does not offer much of value-added in comparison to the current GATS commitments in terms of services liberalisation. The juiciest "carrot" of the Association Agreement - the internal market treatment - is difficult to reach due to unclear and complicated rules on regulatory approximation

Quantitative image analysis for the detection of motion artefacts in coronary artery computed tomography

Multi detector-row CT (MDCT), the current preferred method for coronary artery disease assessment, is still affected by motion artefacts. To rule out motion artefacts, qualitative image analysis is usually performed. Our study aimed to develop a quantitative image analysis for motion artefacts detection as an added value to the qualitative analysis. An anthropomorphic moving heart phantom with adjustable heart-rate was scanned on 64-MDCT and dual-source-CT. A new software technique was developed which detected motion artefacts in the coronaries and also in the myocardium, where motion artefacts are more apparent; with direct association to the qualitative analysis. The new quantitative analysis managed to detect motion artefacts in phantom scans and relate them to artefact-induced vessel stenoses. Quantifying these artefacts at corresponding locations in the myocardium, artefact-induced vessel stenosis findings could be avoided. In conclusion, the quantitative analysis together with the qualitative analysis rules out artefact-induced stenosis

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

GEMINI: A Natural Language System for Spoken-Language Understanding

Gemini is a natural language understanding system developed for spoken language applications. The paper describes the architecture of Gemini, paying particular attention to resolving the tension between robustness and overgeneration. Gemini features a broad-coverage unification-based grammar of English, fully interleaved syntactic and semantic processing in an all-paths, bottom-up parser, and an utterance-level parser to find interpretations of sentences that might not be analyzable as complete sentences. Gemini also includes novel components for recognizing and correcting grammatical disfluencies, and for doing parse preferences. This paper presents a component-by-component view of Gemini, providing detailed relevant measurements of size, efficiency, and performance.Comment: 8 pages, postscrip