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检索条件"主题词=A-priori algorithm"

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Modeling of Heart Data using PSO and a-priori algorithm for Disease Prediction 5

Modeling of Heart Data using PSO and A-Priori Algorithm for ...

引用

5th International Conference on Image Information Processing (ICIIP)

作者： Goel, Shimpy Singh, Rajiv Banasthali Vidyapith Dept Comp Sci Vanasthali Rajasthan India

ISBN: (纸本)9781728108995

Medical organizations such as hospitals include a lot of data and hidden Information. The medical field deals with a lot of data regularly. However, this data is not used correctly. Thus, this unused data can be converted into useful information by using various data extraction techniques. The main disease that causes sudden death of people is heart disease in medicine. It is imperative to predict this disease at an early stage. Large data processing by traditional means can affect results. Computer assisted systems help the doctor as a tool for prediction and analytic exploration of the heart disease. Advanced data mining algorithms and technique are used to discover the facts in the database and for medical research, especially in case of heart disease. This article has main purpose of understanding the datasets which are generally taken in medical field. The understanding of data within the attributes of disease is necessary for developing or modeling the proper algorithm for computing accuracy from the given data sets. The mining association is one of the most important data extraction techniques. Mining link rules can be used efficiently in any rule-building decisions based on decision-making wizard. In this article, we offer effective PSO optimization along with A-priori techniques to model data sets for prediction of heart diseases. Clustering provides the nearby occurrence and nature of datasets as considering the centroid of each cluster for disease. This paper presents the PSO, KMean clustering and the simple statistics which apply over the heart image data sets to provide the result for comparative analysis of proposed clustering technique.

关键词： Heart disease prediction PSO K-Mean a-priori algorithm heart datasets

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A Data Mining approach to predict users' Next Question in QA system 2

A Data Mining approach to predict users' Next Question in QA...

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2nd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Madaan, Rosy Sharma, A. K. Dixit, Ashutosh YMCAUST Dept Comp Engn Faridabad Haryana India BSAITM Dept Comp Sci & Engn Faridabad Haryana India

ISBN: (纸本)9789380544168

In a Question Answering system, the user submits a question and waits for the answer as the response. If the system is capable of predicting the user's future interest as the next question, its performance will improve greatly. This paper predicts users' future questions based on the current interaction records of the user with the system. Their current interactions with the system show what they are interested in. These interaction records are maintained in the form of Questions log from which the user sessions are extracted. Based on the user sessions, the system predicts the next question for which the user may become interested in near future. A sample Questions Log is selected for the purpose of performing experiments. The model of Association rule mining is applied to predict the future question of the user

关键词： Search engines QA system Data mining Association rule mining a-priori algorithm

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An Approach to Mine Significant Frequent Patterns by Quantity Attribute

An Approach to Mine Significant Frequent Patterns by Quantit...

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4th International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rathod, Arti Dhabariya, Ajaysingh Thacker, Chintan Rajasthan Tech Univ Shrinathji Inst Tech Educ Dept Comp Sci Nathdwara India Kera Gujarat Technol Univ HJD Inst Engn Dept Comp Sci Bhuj India

ISBN: (纸本)9781479930708

Mining of association rules or frequent patterns has become an important topic in the research of data mining. However the classical Apriori algorithm is used to mine a frequent pattern which is based on Support-Confidence criteria. But it does not mine significant frequent patterns from the transactional database if Quantity, Profit and weight attributes are there. So, this paper introduces a new approach which extracts significant frequent patterns by considering quantity attributes and by applying Q-factor and S-factor to the transactional database. Q-ratio is the ratio of quantity of particular items throughout all transaction to the total quantity of all items of all transaction and S-factor is the product of Q-ratio of particular items and the frequency of particular items throughout all transaction.

关键词： Association Significant frequent patterns a-priori algorithm Support Confidence Q-Ratio S-Factor

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The Analysis of Water and Health Database by One Improved a-priori algorithm

The Analysis of Water and Health Database by One Improved A-...

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International Conference on Environmental Science and Information Application Technology (ESIAT 2009)

作者： Jinsheng Liu E China Inst Technol Sch Civil & Environm Engn Fuzhou Peoples R China

ISBN: (纸本)9780769536828

Water quality and local illnesses have some relationships which can not be predicted by simple statistical algorithm so one improved a-priori algorithm is introduced to objectively meet this question. a-priori algorithm, which can efficiently rind useful associational relationships among databases, is one popular algorithm in associational rule mining. Moreover, this paper introduced some useful and extended conceptions to improve the efficiency of a-priori algorithm, such as independent support counts and error support count on these properties. Statistical graph of database is applied in the experience of database to directly describe the relationship between elements in pollution water and illness. At the same time, many experiences on mining associational rules are done so as to test the efficiency of algorithm. These experiences proved that statistical graph of database can straightforward discover associational rules on elements in water and illnesses in database.

关键词： Statistical graph of database independent support count associational rules a-priori algorithm

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The research of a-priori algorithm Candidates Based on Support Counts

The research of A-Priori Algorithm Candidates Based on Suppo...

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International Conference on Information Technology and Computer Science

作者： Zhou, Huanyin Liu, Jinsheng Shenyang Inst Automat East China Inst Technol State Key Lab Robot Fuzhou Peoples R China East China Inst Technol Sch Environm Engineer Fuzhou Peoples R China

ISBN: (纸本)9780769536880

Apriori algorithm is one of the most standard, popular way among associational rule mining algorithms. Its famous character 'all the sub-frequent itemsets are frequent itemsets, and no one itemset is frequent if its sub-frequent is infrequent itemsets' has been reduction the number of candidate itemsets, but the number of candidate itemsets is still large. Moreover, the ratio is so small between frequent itemsets and candidate itemsets that the largest time is wasted in checking the candidate itemsets. According to the character of Apriori, this paper sums up three regularities to improve the ratio of useful candidates. At the same time the independent support count is cited to prune the frequent itemsets who are independent their father itemsets. The optimize algorithm has been proved and realized by one database. This paper is arranged as the following: section 2 presents some key definitions on these rules such as support count, attenuation to support count, constant support count on fixed itemsets and three rules will be introduced shortly. Demonstrations and applications on these rules will be detailed in section 3. Section 4 introduces the algorithm of rules and an example is introduced to test the efficiency of these rules. The last section draws some conclusions and future works.

关键词： a-priori algorithm associational rule constant onfix item support count independent support count

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Laws on Support Counts of Apriori algorithm Candidates

Laws on Support Counts of Apriori Algorithm Candidates

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1st IITA International Joint Conference on Artificial Intelligence

作者： Zhou, Huanyin Liu, Jinsheng Shenyang Inst Automat East China Inst Technol State Key Lab Robot Fuzhou Peoples R China East China Inst Technol Sch Environm Engineer Fuzhou Peoples R China

ISBN: (纸本)9780769536156

This paper proposes three novel laws for finding useful candidates in database and preventing useless candidates by researching frequent itemset support. Some new concepts are introduced so as to explain these three laws. For example, independent itemset and support count, which can effectively avoid losing any interest associational rules during pruning, are introduced. In this paper, firstly, some key definitions on these approaches are presented such as support count, constant support count on fixed item sets then three novel approaches are detailed. Secondly, demonstrations and applications on these approaches are presented. The application of these approaches is used to test significances of these laws by an example which proves that these novel approaches can more efficiently reduce redundant candidates than a-priori algorithm.

关键词： a-priori algorithm associational rule constant on fix item support count independent support count

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Rule Modeling Engine for Optimizing Complex Event Processing Patterns

Rule Modeling Engine for Optimizing Complex Event Processing...

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International Conference of Soft Computing and Pattern Recognition

作者： Behravesh, Babak Shamsuddin, Siti Mariyam Zainal, Anazida Univ Teknol Malaysia Fac Comp Sci & Informat Syst Skudai 81310 Malaysia

ISBN: (纸本)9781424453306

In Complex Event Processing (CEP), we deal with how to search through a sequence of incoming events to find a specified and desired pattern. CEP has a broad use in today enterprise. It can act on sent and/or received events. The result can generate other events that can be used in different layers of an enterprise system. Growing number of areas dealing with arisen events like Business Activity Monitoring (BAM), Fraud detection and intrusion detection makes CEP a hot topic for researchers. Generating efficient high-performance patterns is the issue which has been addressed in this paper. The pattern can be made from any query given by user. The user defined query is CQL (Continuous Query Language) which is relevant for time series data. NFA (Non-deterministic Finite Automaton) is used for modeling patterns although it has some defects which are addressed The focus of this paper is on developing a rule modeling engine and taking into account the role of historical data to make efficient patterns. We developed some algorithms for each component of proposed model. The results are optimized patterns produced based on historical data and queries given by user. Finally we show that these techniques can be efficient when we deal with high volume event-base data.

关键词： Data Mining Association Rules a-priori algorithm Event streams Pattern matching Query optimization

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A Novel algorithm for Association Rule Mining Without Candidate

A Novel Algorithm for Association Rule Mining Without Candid...

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1st IITA International Joint Conference on Artificial Intelligence

作者： Zhou, Huanyin Liu, Jinsheng Shenyang Inst Automat E China Inst Technol State Key Lab Robot Fuzhou Peoples R China East China Inst Technol Sch Environm Engineer Fuzhou Peoples R China

ISBN: (纸本)9780769536156

A-priori is an influence algorithm for finding frequent itemsets from association rules. But there are two hard questions may be involved for average users during finding frequent candidates. One question is massive amounts of candidates and the other is that set support count threshold for every level candidate generations. This paper discusses one algorithm called And, which is usually used in logical algorithms, And Code (AC) algorithm can discover frequent itemsets without producing candidates and setting thresholds for candidates. Frequent itemsets can be fast discovered by corresponding codes which are cited by this paper to describe different itemsets for AC algorithm. The support count of frequent itemsets can be computed before the process of scanning during processes of AC algorithm. The codes of itemsets are defined and detailed in section two. Lastly, an example is presented to detail processes of AC algorithm and test that AC algorithm can more efficiently find frequent without candidates than a-priori algorithm which may produce a large of candidates during scan process.

关键词： association rule mining And Code algorithm frequent itemsets a-priori algorithm

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A new method for automatically constructing concept maps based on data mining techniques

A new method for automatically constructing concept maps bas...

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7th International Conference on Machine Learning and Cybernetics

作者： Bai, Shih-Ming Chen, Shyi-Ming Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781424420957

In recent years, some methods have been presented for dealing with concept maps construction to provide adaptive learning guidance to learners. In this paper, we present a new method to automatically construct concept maps based on the a-priori algorithm and the analysis of the relationships between concepts. The proposed method provides a useful way to automatically construct concept maps in adaptive learning systems.

关键词： adaptive learning a-priori algorithm concept maps questions-relationship mining concepts-relationship analysis

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Scouting for imprecise temporal associations to support effectiveness of drugs during clinical trials

Scouting for imprecise temporal associations to support effe...

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Annual Meeting of the North-American-Fuzzy-Information-Processing-Society

作者： Vincenti, G Hammell, RJ Trajkovski, G Towson Univ Dept Comp & Informat Sci Towson MD 21252 USA

ISBN: (纸本)078039187X

The field of data mining is dedicated to the analysis of data to find underlying connections and the discovery of new patterns. This research targets the analysis of imprecise temporal associations through the modification of a standard Market Basket Analysis approach by means of fuzzy set relations to classify the associations among different sources of data. The domain that is taken into consideration in this work is the one of medicine. We used data recorded within an Intensive Care Unit from a 8 month old infant that suffers from Acute Respiratory Distress Syndrome. In particular, we analyzed the response of the partial pressure of oxygen within the bloodstream to the application of a respirator. The results of this research show that it is possible to investigate such relations with the help of fuzzy set classification for temporal associations, and the result of such exploration is as easily understandable as the standard Market Basket algorithm. The findings support the physiological response, suggesting that this approach is worthy of notice. We are confident that such an algorithm will show its capabilities when applied to the clinical trials part of drug testing, given the results outlined in this article.

关键词： data mining market-basket algorithm a-priori algorithm temporal associations medical informatics

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