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2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
2018 10th International Conference on Communication Software and Networks (ICCSN 2018) will be held during July 6-9, 2018 in Chengdu, China. ICCSN 2018 is sponsored by University of Electronic Science and Technology of China, co-sponsored by 54th Institute, CETC, China, Science and Technology on Communication Networks Laboratory, supported by Guangdong University of Technology, China and AET Journal.
Computational Intelligence techniques typically include Fuzzy Logic, Evolutionary Computation, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems and other similar computational models. The application of computational intelligence techniquesinto industrial design, interactive design, media design, and engineering design are also within the scope.
Conference Theme:AI Empowering the Future Education, the scope of the conference includes: Computer science,Data Science,Educational Technology etc.
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
Signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, andcolor reproduction.
Artificial intelligence techniques, including speech, voice, graphics, images, and documents; knowledge and data engineering tools and techniques; parallel and distributed processing; real-time distributed processing; system architectures, integration, and modeling; database design, modeling, and management; query design, and implementation languages; distributed database control; statistical databases; algorithms for data and knowledge management; performance evaluation of algorithms and systems; data communications aspects; system ...
IEEE Network was the number one most-cited journal in telecommunications, the number twelve most-cited journal in electrical and electronics engineering, and the number three most-cited journal in Computer Science Hardware and Architecture in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers topics which include: ...
All telecommunications, including telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space, and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; communication theory; and wireless communications.
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006
Association rule mining is an active data mining research area. Recent years have witnessed many efforts on discovering fuzzy associations. The key strength of fuzzy association rule mining is its completeness. This strength, however, comes with a major drawback to handle large datasets. It often produces a huge number of candidate itemsets. The huge number of candidate itemsets makes it ...
2002 IEEE International Conference on Data Mining, 2002. Proceedings., 2002
Mining association rules may generate a large numbers of rules making the results hard to analyze manually. Pasquier et al. have discussed the generation of Guigues-Duquenne-Luxenburger basis (GD-L basis). Using a similar approach, we introduce a new rule of inference and define the notion of association rules cover as a minimal set of rules that are non-redundant with respect to ...
KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516), 2000
Proposes the concept of pre-large item sets and designs a novel, efficient incremental data mining algorithm based on it. Pre-large item sets are defined using two support thresholds (a lower support threshold and an upper support threshold) to reduce re-scanning of the original databases and to save maintenance costs. The proposed algorithm doesn't need to re-scan the original database until ...
2008 11th IEEE International Conference on Communication Technology, 2008
Association rules are the main technique for data mining. Apriori algorithm is a classical algorithm of association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on basis of apriori algorithm, but traditional algorithms are not efficient. For the two bottlenecks of frequent itemsets mining: the large multitude of candidate 2-itemsets, the poor efficiency of ...
2007 International Conference on Computational Intelligence and Security (CIS 2007), 2007
Association rules mining is one of the important tasks in data mining research. While most of the existing discovery algorithms are dedicated to efficiently mining of frequent patterns, it has been noted recently that some of the infrequent patterns can provide useful insight view into the data. As a result, indirect association rules have been put forward, the traditional association ...
Association rule mining is an active data mining research area. Recent years have witnessed many efforts on discovering fuzzy associations. The key strength of fuzzy association rule mining is its completeness. This strength, however, comes with a major drawback to handle large datasets. It often produces a huge number of candidate itemsets. The huge number of candidate itemsets makes it ineffective for a data mining system to analyze them. To overcome this problem in this study, fuzzy association rule mining system is driven by domain ontology. It describes the use of a concept hierarchy for improving the results of fuzzy association rule mining. Our ontology-based data mining algorithm makes the rules more visual, more interesting and more understandable. At last the paper, the efficiency and advantages of this algorithm has been approved by experimental results
Mining association rules may generate a large numbers of rules making the results hard to analyze manually. Pasquier et al. have discussed the generation of Guigues-Duquenne-Luxenburger basis (GD-L basis). Using a similar approach, we introduce a new rule of inference and define the notion of association rules cover as a minimal set of rules that are non-redundant with respect to this new rule of inference. Our experimental results (obtained using both synthetic and real data sets) show that our covers are smaller than the GD-L basis and they are computed in time that is comparable to the classic Apriori algorithm for generating rules.
Proposes the concept of pre-large item sets and designs a novel, efficient incremental data mining algorithm based on it. Pre-large item sets are defined using two support thresholds (a lower support threshold and an upper support threshold) to reduce re-scanning of the original databases and to save maintenance costs. The proposed algorithm doesn't need to re-scan the original database until a number of transactions have arrived. If the size of the database is growing larger, then the allowed number of new transactions will be larger too. Therefore, along with the growth of the database, our proposed approach is increasingly efficient. This characteristic is especially useful for real applications.
Association rules are the main technique for data mining. Apriori algorithm is a classical algorithm of association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on basis of apriori algorithm, but traditional algorithms are not efficient. For the two bottlenecks of frequent itemsets mining: the large multitude of candidate 2-itemsets, the poor efficiency of couting their support, this paper proposes a novel algorithm so called reduced apriori algorithm with tag (RAAT), which reduces one redundant pruning operations of C2. If the number of frequent 1-itemsets is n, then the number of connected candidate 2-itemsets is Cn 2, while pruning operations Cn 2. The novel algorithm decreases pruning operations of candidate 2-itemsets, thereby saving time and increasing efficiency.For the bottleneck:poor efficiency of couting support, RAAT optimizes subset operation, through the transaction tag to speed up support calculations. The experimental results obtained from tests show that RAAT outperforms original one efficiency.
Association rules mining is one of the important tasks in data mining research. While most of the existing discovery algorithms are dedicated to efficiently mining of frequent patterns, it has been noted recently that some of the infrequent patterns can provide useful insight view into the data. As a result, indirect association rules have been put forward, the traditional association rules are called direct association patterns. All the existing algorithms for mining indirect association rules need get all frequent itemsets using Apriori or other algorithms for mining association rules, then generate indirect association candidates using frequent itemsets. Instead of this method, we put forward an approach to discover both direct and indirect association patterns. Key words: Direct Association Pattern, Indirect Association Pattern, Data Mining
Rough Set is an important theory of AI. In this paper, Rough_Apriori Algorithm which is based on the classic Apriori Algorithm and Rough Set theory. The Basic concepts and Algorithm steps of Rough_Apriori were Defined. In accordance with a set of definitions, an aid system of campus student major selection was developed. By comparing with user-expected results, the ultimate experimental results demonstrates the efficiency of this system and the algorithm.
A key issue in mining association rules is to find out all frequent itemsets, therefore how to mine frequent itemsets quickly has been hot in current research. Mining algorithms of the maximal frequent itemsets based on FP-trees necessitate not only the multiple generations of large numbers of FP-trees, but also the multiple traversals of these FP-trees, thus taking much time. Against the above shortcomings, we propose an FP-tree-based algorithm MMFI optimized with array and matrix for mining the maximal frequent itemsets. It not only reduces the quantity of the FP-trees constructed, but also saves the time in traversing the FP-trees. Finally, we have compared the algorithm MMFI with the algorithm FP-MAX, the results of our experiment have shown that this algorithm is working efficiently.
This paper introduces a fusion model to reinforce multi-level fuzzy association rules, which integrated cumulative probability distribution approach (CPDA) and multi-level taxonomy concepts to extract fuzzy association rules. The proposed model generate large item sets level by level and mine multi-level fuzzy association rule lead to finding more informative and important knowledge from transaction dataset, which is more objective and reasonable in determining the universe of discourse and membership functions with other multi-level fuzzy association rules.
In dynamic databases, new transactions are appended as time advances. This may introduce new association rules and some existing association rules would become invalid. Thus, the maintenance of association rules for dynamic databases is an important problem. In this paper, probability-based incremental association rule discovery algorithm is proposed to deal with this problem. The proposed algorithm uses the principle of Bernoulli trials to find expected frequent itemsets. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has a good performance.
Intertransaction frequent itemsets break the barriers of transactions, and extend the traditional single-dimensional intratransaction association rules to multidimensional intertransaction association rules. However the amount of intertransaction frequent itemsets becomes very large with the increase of the sliding window. Frequent closed itemsets can uniquely determine the set of all frequent itemsets and their exact frequency while they are far smaller than all frequent itemsets. In this paper, we introduce the notion of intertransaction frequent closed itemset, analyze its properties, and develop an efficient algorithm, IFCIA (intertransaction frequent closed itemsets algorithm). The algorithm adopts division-based method and condition database to avoid generating large extended database, and uses bitmap structure and extended bitwise operations to generate candidate itemsets and count the support quickly. Experiments on real and synthetic databases show that IFCIA is an effective algorithm for mining intertransaction frequent closed itemsets.
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