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15th International Conference on Date Engineering

作者： Han, JW Dong, GZ Yin, YW Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)0769500714

Partial periodicity search, i.e., search for partial periodic patterns in time-series databases, is an interesting data mining problem. Previous studies on periodicity search mainly consider finding full periodic patterns, where every point in time contributes (precisely or approximately) to the periodicity. However, partial periodicity is very common in practice since it is more likely that only some of the time episodes may exhibit periodic patterns. We present several algorithms for efficient mining of partial periodic patterns, by exploring some interesting properties related to partial periodicity, such as the Apriori property and the max-subpattern hit set property, and by shared mining of multiple periods. The max-subpattern hit set property is a vital new property which allows las to derive the counts of all frequent patterns from a relatively small subset of patterns existing in the time series. We show that mining partial periodicity needs only two scans over the time series database, even for mining multiple periods. The performance study shows our proposed methods are very efficient in mining long periodic patterns.

关键词： periodicity search partial periodicity time-series analysis data mining algorithms

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Research on the Impact and Mechanism of Artificial Intelligence on Consumption Upgrading

Research on the Impact and Mechanism of Artificial Intellige...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Zhu, Jixu Chen, Xiaoshi Guangzhou City University of Technology Guangdong Province Guangzhou510800 China

ISBN: (纸本)9798350382693

As the only way to comprehensively expand domestic demand, consumption upgrading gradually reflects the impact and mechanism of consumption upgrading with the development of artificial intelligence technology. The role of mechanism evaluation in consumption structure under artificial intelligence is very important, but there is a problem of inaccurate outcome evaluation. The traditional consumption model cannot solve the problem of evaluation of the mechanism of consumption structure under artificial intelligence, and the evaluation is unreasonable. Therefore, this paper proposes a data mining algorithm to evaluate and analyze the mechanism of optimization and innovation. Firstly, the relative income consumption theory is used to evaluate the consumer market, and the indicators are divided according to the requirements of the mechanism evaluation to reduce the interference factors in the evaluation of the mechanism. Then, the relative income consumption theory evaluates the mechanism of consumption upgrading level, forms a mechanism evaluation scheme, and comprehensively analyzes the evaluation results of the mechanism. MATLAB simulation shows that under certain evaluation criteria, the evaluation accuracy of the mechanism of data mining algorithms on the level of consumption upgrading and the quality of consumption structure transformation and upgrading are better than those of traditional consumption patterns. © 2024 IEEE.

关键词： Artificial intelligence consumption structure data mining algorithms relative income consumption theory

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Research on the application of data mining algorithm in photojournalism and short video communication

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第5期11卷

作者： Wang, Yidi Zhang, Yan Song Dalian Polytech Univ Sch Fash Dalian 116034 Liaoning Peoples R China

Short films and photojournalism are crucial parts of video communication, yet there are issues including poor video integration, inconsistency between short videos and photojournalism, and slow communication speed. In order to evaluate and analyse video communication, this research offers a data mining algorithm. First, the database's images and videos are chosen and analysed using a mining algorithm, and then the indicators are reduced by dividing them into groups based on the specifications for video dissemination. video transmission distractions. The mining algorithm then analyses the video propagation, creates a plan that satisfies the criteria, and refines the plans that satisfies the criteria. Analyse. The integration level, timeliness, and compliance rate of data mining algorithms for video propagation are superior to traditional video dissemination method under specific analysis criteria, according to MATLAB simulation.

关键词： photojournalism data mining algorithms short videos Video dissemination

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data mining Perspective of Educational Big data

Data Mining Perspective of Educational Big Data

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第24屆全球华人计算机教育应用大会(GCCCE 2020)

作者： Jinjin Liang Yong Nie Hongying Gao Mengyang Sun School of Education Shaanxi Normal University School of Sciences Xi'an Shiyou University School of Education Shaanxi Normal University

Educational Big data(EBD)is coming due to the emerging of large amount of learning data in the education field,and promotes a research hotspot called Educational data mining(EDM).This paper focuses on current researches from the data mining perspective,summarizes the traditional applications scenes,classifies the data mining algorithms into descriptive and predictive ones,and designs a framework for educational big *** proposed approach gives a clear vision and points out challenges and directions for further research in EBD.

关键词： educational big data educational data mining applications scenes data mining algorithms

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BISC: A Bitmap Itemset Support Counting Approach for Efficient Frequent Itemset mining

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM data 2010年第3期4卷 12-12页

作者： Chen, Jinlin Xiao, Keli CUNY Queens Coll Dept Comp Sci Flushing NY 11367 USA Rutgers State Univ Rutgers Business Sch Newark NJ 07102 USA

The performance of a depth-first frequent itemset (FI) miming algorithm is closely related to the total number of recursions. In previous approaches this is mainly decided by the total number of FIs, which results in poor performance when a large number of FIs are involved. To solve this problem, a three-strategy adaptive algorithm, bitmap itemset support counting (BISC), is presented. The core strategy, BISC1, is used in the innermost steps of the recursion. For a database D with only s frequent items, a depth-first approach need up to s levels of recursions to detect all the FIs (up to 2(s)). BISC1 completely replaces these recursions with a special summation that directly calculates the supports of all the possible 2s candidate itemsets. With BISC1 the run-time is entirely independent of the database after one database scan, and the per-candidate cost is only s. To offset the exponential growth of cost (both time and space) with BISC1 as s increases, a second strategy, BISC2, is introduced to effectively double the acceptable range of s. BISC2 divides an itemset into prefix and suffix and improves the performance by pruning all the itemsets with infrequent prefixes. If the total number of frequent items in D is high, the classic database projection strategy is used. In this case for the first s items a single run of BISC (1 or 2) is applied. For each of the remaining items, a projected database is created and the mining process proceeds recursively. To achieve optimal performance, BISC adaptively decides which strategy to use based on the dataset and minimum support. Experiments show that BISC outperforms previous approaches in all the datasets tested. Even though this does not guarantee that BISC will always perform the best, the result is impressive given the fact that most existing algorithms are only efficient in some types of datasets. The memory usage of BISC is also comparable to those of other algorithms.

关键词： data mining algorithms frequent itemset mining association rule mining

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RETRACTED: Dynamic monitoring and management system for land resource based on parallel network algorithm and remote sensing (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第1期37卷 249-262页

作者： Xu, Zhou Anhui Univ Finance & Econ Bengbu Anhui Peoples R China

With the acquisition of remote sensing data, the improvement of image resolution, and the further development of remote sensing application and computer technology, remote sensing has become an important means of land avalanche investigation. Using GIS and spatial database technology, this paper designs and develops the land use dynamic remote sensing monitoring database system. At the same time, the author studies the key technology of the system, and constructs a massive spatial database of land geography. The construction of the platform is mainly divided into two aspects, one is the audit and distribution system on the web side and the other is the mobile side to investigate APP. Web-side system mainly realizes image viewing and verification of investigation results. Through this platform, image data can be efficiently distributed to users in different areas through the network, providing users with the functions of spatial data browsing, query and analysis, and fully sharing data. The effect of data mining and remote sensing technology in land resource management is obvious, which promotes the development of land resource management in the direction of intellectualization and refinement. The data of the research results in this paper will be the basis of land management. By mastering the precise utilization of land resources, we can further improve the precision level of land management and promote the sustainable development of economy and society.

关键词： data mining algorithms remote sensing technology land resource management cloud data

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A Personalized Webpage Reconstructor Based on Concept Lattice and Association Rules

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JOURNAL OF INTERNET TECHNOLOGY 2011年第6期12卷 1015-1024页

作者： Kuo, Rita Hsu, Chang-Kai Chang, Maiga Heh, Jia-Sheng Chung Yuan Christian Univ Dept Informat & Comp Engn Zhongli City Taiwan Athabasca Univ Sch Comp & Informat Syst Athabasca AB Canada

Efficiency issue in automatic web-based information retrieval research becomes an important issue to users. Most knowledge and materials on the Internet is in either semi-structured or unstructured hypermedia form. When users found a webpage as searching result from the Internet for self-learning, sometimes they may not understand the meanings of specific part of the retrieved webpage easily. They need to spend a lot of time in finding more references manually to make they get clear idea of the original retrieved webpage told. If an agent can be developed to reconstruct the webpage that the users are browsing by inserting additional self-explainable documents' links at appropriate places in the original webpage, it will be perfect. This research uses formal concept analysis (FCA) and association rule methodology (ARM) to develop a Keyword Association Lattice (KAL). With the KAL, the webpages accessed by users can be analyzed and reconstructed automatically. A pedagogical software agent called K-Navi for users doing survey and self-learning on the Internet is developed.

关键词： Pedagogical software agent data mining algorithms Keyword query Hypermedia Knowledge-Based software

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Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings

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ENERGIES 2019年第15期12卷 2933页

作者： Cerquitelli, Tania Malnati, Giovanni Apiletti, Daniele Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy

The pervasive and increasing deployment of smart meters allows collecting a huge amount of fine-grained energy data in different urban scenarios. The analysis of such data is challenging and opening up a variety of interesting and new research issues across energy and computer science research areas. The key role of computer scientists is providing energy researchers and practitioners with cutting-edge and scalable analytics engines to effectively support their daily research activities, hence fostering and leveraging data-driven approaches. This paper presents SPEC, a scalable and distributed engine to predict building-specific power consumption. SPEC addresses the full analytic stack and exploits a data stream approach over sliding time windows to train a prediction model tailored to each building. The model allows us to predict the upcoming power consumption at a time instant in the near future. SPEC integrates different machine learning approaches, specifically ridge regression, artificial neural networks, and random forest regression, to predict fine-grained values of power consumption, and a classification model, the random forest classifier, to forecast a coarse consumption level. SPEC exploits state-of-the-art distributed computing frameworks to address the big data challenges in harvesting energy data: the current implementation runs on Apache Spark, the most widespread high-performance data-processing platform, and can natively scale to huge datasets. As a case study, SPEC has been tested on real data of an heating distribution network and power consumption data collected in a major Italian city. Experimental results demonstrate the effectiveness of SPEC to forecast both fine-grained values and coarse levels of power consumption of buildings.

关键词： big data frameworks data mining algorithms machine learning energy consumption forecast data streams analysis

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Predicting Large Scale Fine Grain Energy Consumption

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Energy Procedia 2017年 111卷 1079-1088页

作者： Tania Cerquitelli Control and Computer Engineering Department Politecnico di Torino C.so Duca degli Abruzzi 24 10129 Torino ITALY

Today a large volume of energy-related data have been continuously collected. Extracting actionable knowledge from such data is a multi-step process that opens up a variety of interesting and novel research issues across two domains: energy and computer science. The computer science aim is to provide energy scientists with cutting-edge and scalable engines to effectively support them in their daily research activities. This paper presents SPEC, a scalable and distributed predictor of fine grain energy consumption in buildings. SPEC exploits a data stream methodology analysis over a sliding time window to train a prediction model tailored to each building. The building model is then exploited to predict the upcoming energy consumption at a time instant in the near future. SPEC currently integrates the artificial neural networks technique and the random forest regression algorithm. The SPEC methodology exploits the computational advantages of distributed computing frameworks as the current implementation runs on Spark. As a case study, real data of thermal energy consumption collected in a major city have been exploited to preliminarily assess the SPEC accuracy. The initial results are promising and represent a first step towards predicting fine grain energy consumption over a sliding time window.

关键词： energy data big data frameworks data mining algorithms data stream analysis

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Comparison of Signal Detection algorithms Based on Frequency Statistical Model for Drug-Drug Interaction Using Spontaneous Reporting Systems

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PHARMACEUTICAL RESEARCH 2020年第5期37卷 86-86页

作者： Noguchi, Yoshihiro Tachi, Tomoya Teramachi, Hitomi Gifu Pharmaceut Univ Lab Clin Pharm 1-25-4 Daigakunishi Gifu 5011196 Japan Gifu Pharmaceut Univ Lab Community Healthcare Pharm 1-25-4 Daigakunishi Gifu 5011196 Japan

Purpose Adverse events (AEs) caused by polypharmacy have recently become a clinical problem, and it is important to monitor the safety profile of drug-drug interactions (DDIs). mining signals using the spontaneous reporting systems is a very effective method for single drug-induced AE monitoring as well as early detection of DDIs. The objective of this study was to compare signal detection algorithms for DDIs based on frequency statistical models. Methods Five frequency statistical models: the omega shrinkage measure, additive (risk difference), multiplicative (risk ratio), combination risk ratio, and chi-square statistics models were compared using the Japanese Adverse Drug Event Report (JADER) database as the spontaneous reporting system in Japan. The drugs targeted for the survey are all registered and classified as "suspect drugs" in JADER, and the AEs targeted for this study were the same as those in a previous study on Stevens-Johnson syndrome (SJS). Results Of 3924 pairs that reported SJS, the number of signals detected by the omega shrinkage measure, additive, multiplicative, combination risk ratio, and chi-square statistics models was 712, 3298, 2252, 739, and 1289 pairs, respectively. Among the five models, the omega shrinkage measure model showed the most conservative signal detection tendency. Conclusion Specifically, caution should be exercised when the number of reports is low because results differ depending on the statistical models. This study will contribute to the selection of appropriate statistical models to detect signals of potential DDIs.

关键词： data mining algorithms drug-drug interaction frequency statistical model signal detection spontaneous reporting systems

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