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检索条件"主题词=Data mining Algorithm"

共 76 条记录，以下是61-70 订阅

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A Proposal of Spatio-Temporal Pattern Queries

A Proposal of Spatio-Temporal Pattern Queries

引用

4th International Conference on Complex, Intelligent and Software Intensive Systems (CICIS)

作者： Gorawski, Marcin Jureczek, Pawel Silesian Tech Univ Inst Comp Sci Akad 16 PL-44100 Gliwice Poland

ISBN: (纸本)9780769539676

This paper provides a comprehensive analysis of algorithms for spatio-temporal pattern mining, which helps to select appropriate algorithms for a given Moving Objects database. We can distinguish basic and extended comparing functions. The basic ones correspond to research fields related to pattern mining. The extended comparing functions extend the analysis to additional aspects. All these functions are presented in the context of pattern queries for Moving Objects databases. In addition, comparing functions can be combined into more complex ones and used in frequent pattern mining algorithms in order to obtain various summaries of all analyzed sequences. We also present two algorithms, called VGES (Vertical GSP for ExactSearch) and SES (Spade for ExactSearch), for continuous sequential pattern mining. In the paper different methods of searching for frequent patterns are also compared.

关键词： data mining algorithm pattern query contiguous sequential pattern performance analysis region of interest

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Discovering local patterns from multiple temporal sequences

引用

1st EurAsian Conference on Advances in Information and Communication Technology (EurAsia-ICT 2002)

作者： Jin, XM Lu, YC Shi, CY Tsing Hua Univ Comp Sci & Technol Dept Natl Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

ISBN: (纸本)3540000283

In this paper, we address a data-mining problem that is the discovery of local sequential patterns from a set of long sequences. Each local sequential pattern is represented by a pattern A-->B and a time period in which A-->B is frequent. Such patterns are actually very common in practice and are potentially very useful. However it is impractical to use traditional methods on this problem directly. We propose a suffix-tree-like data structure for indexing the instances of the patterns. Based on this index, our mining method can discover all locally frequent patterns after one scan of the sequences. We have analyzed the behavior of the problem and evaluated the performance of our algorithm with both synthetic and real data. The results correspond with the definition of the problem and verify the superiority of our approach.

关键词： local sequential pattern temporal sequence data mining algorithm

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Description of Aggregate System Clustering algorithm and Its Application in the Analysis of Automobile Fault Law 5

Description of Aggregate System Clustering Algorithm and Its...

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IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Kang, Hao Zhao, Hailong Ai, Ting Army Acad Armored Forces Beijing Peoples R China

ISBN: (纸本)9781728143231;9781728143224

Cluster analysis is an important means of data analysis and knowledge discovery, which provides an important basis for revealing the inherent differentiation and correlation between data. This paper expounds the data characteristics, analysis significance and hierarchical model of automobile fault rules, and describes the optimization algorithm based on system cluster analysis. The algorithm is applied to classify and analyze the data information of personal driving habits and specific faults, which provides the premise and foundation for the follow-up research on the correlation between human induced factors and faults.

关键词： data mining algorithm system cluster analysis optimization principle spectrum diagram classification of data sets fault law failure data

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Classification Based on A Multi-Dimensional Probability Distribution and Its Application to Network Intrusion Detection

Classification Based on A Multi-Dimensional Probability Dist...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Mabu, Shingo Li, Wenjing Lu, Nannan Wang, Yu Hirasawa, Kotara Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781424481262

With the rapid growth of the Internet, to make sure of the computer security has been a crucial problem, therefore, many techniques for Intrusion detection have been proposed in order to detect network attacks efficiently. On the other hand, data mining algorithms based on Genetic Network Programming (GNP) have been proposed recently. GNP is a graph-based evolutionary algorithm and can extract many important class association rules by making use of the distinguished representation ability of the graph structures. In this paper, a probabilistic classification is proposed and combined with the class association rule mining of GNP, and applied to Network intrusion detection for the performance evaluation. The proposed method creates a joint probability density function of normal and intrusion accesses and use it to efficiently classify new access data into normal, known intrusion or unknown intrusion. It is clarified from the experimental results that the proposed method shows high classification accuracy compared to the method without probabilistic classification.

关键词： Internet Internet class association rule mining class association rules computer security data mining algorithm genetic algorithms genetic network programming graph structures graph-based evolutionary algorithm joint probability density function multidimensional probability distribution network intrusion detection performance evaluation probabilistic classification security of data statistical distributions

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A Two Stage Approach for Contiguous Sequential Pattern mining

A Two Stage Approach for Contiguous Sequential Pattern Minin...

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IEEE International Conference on Information Reuse and Integration

作者： Chen, Jinlin Shankar, Subash Kelly, Angela Gningue, Sergine Rajaravivarma, Rathika CUNY Queens Coll Flushing NY 11367 USA Hunter Coll New York NY USA Lehman Coll CUNY New York NY USA City Tech CUNY New York NY USA

ISBN: (纸本)9781424441150

Contiguous Sequential Pattern (CSP) mining is an important problem with many applications. Using general sequential pattern mining algorithms for CSP mining may lead to poor performance due to the lack of consideration on the contiguous property of CSP. In this paper we present a two stage approach for CSP mining. We first detect frequent itemsets in a database, based on which we partition the CSPs into subsets and apply a special data structure, General UpDown Tree, to detect all the patterns in each subset. The General Updown Tree exploits the contiguous property of CSPs to achieve a compact representation of all the sequences that contain an item. Such compact representation enables us to apply a top down approach for CSP mining and eliminates unnecessary candidate evaluation. Experiment results show that our approach is more efficient compared to previous approaches in terms of both time and space.

关键词： Sequential pattern Contiguous sequential pattern Sequence database data mining algorithm

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Differential Private Random Forest 3

Differential Private Random Forest

引用

3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Patil, Abhijit Singh, Sanjay Manipal Univ Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 Karnataka India

ISBN: (纸本)9781479930807

Organizations be it private or public often collect personal information about an individual who are their customers or clients. The personal information of an individual is private and sensitive which has to be secured from data mining algorithm which an adversary may apply to get access to the private information. In this paper we have consider the problem of securing these private and sensitive information when used in random forest classifier in the framework of differential privacy. We have incorporated the concept of differential privacy to the classical random forest algorithm. Experimental results shows that quality functions such as information gain, max operator and gini index gives almost equal accuracy regardless of their sensitivity towards the noise. Also the accuracy of the classical random forest and the differential private random forest is almost equal for different size of datasets. The proposed algorithm works for datasets with categorical as well as continuous attributes.

关键词： data mining data privacy learning (artificial intelligence) Gini index data mining algorithm differential privacy differential private random forest information gain max operator personal information private information sensitive information Accuracy data privacy Indexes Noise Privacy Sensitivity Vegetation

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Research on Intelligent System of Remote Cooperation Information Sharing in Human Resource Management Based on Wireless Communication Technology 3

Research on Intelligent System of Remote Cooperation Informa...

引用

3rd IEEE International Conference on Electrical Engineering, Big data and algorithms, EEBDA 2024

作者： Zhang, Hong Tang, Wen Liu, Hongwei Liu, Siyuan School of Management Liaoning Communication University Shenyang110136 China School of Arts and Media Shenyang Institute of Technology Liaoning Shenyang113122 China Shenyang Research Institute of Chemical Industry Shenyang110020 China

ISBN: (纸本)9798350380989

Combining Delphi's development method, a HR management system suitable for small and medium-sized enterprises is designed and implemented. According to the actual needs of enterprises, the system is divided into four functional modules, and the data and information flow of each module are designed in detail. The SQL Server 2008 is constructed for this system. The entity, attribute, relationship, connection mode and constraint conditions based on rules are defined, and the personnel information base of the enterprise is constructed, so as to achieve the purpose of personnel management of enterprises. data mining algorithm is introduced to standardize the system documents, standardize the database and connect the database. The scientific decision of personnel scheduling was made. Then the method of data mining is introduced to develop a HR management system suitable for enterprises in order to promote the sustainable development of domestic health undertakings. The effectiveness of this scheme is verified by comparison experiments. The system can realize hundreds of people online at the same time, and there will be no abnormal conditions such as stuttering and flickering. © 2024 IEEE.

关键词： data mining algorithm Human resources remote collaboration scheduling decision system

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Neural Network Prediction of CFG Pile Composite Foundation Bearing Capacity based on BP algorithm

Neural Network Prediction of CFG Pile Composite Foundation B...

引用

2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Caifeng, Jia Baicheng Vocational and Technical College Jilin Province 137000 China

ISBN: (纸本)9798350374933

In the rapid development of neural network prediction today, the use of big data algorithm means to improve the CFG pile composite foundation bearing capacity has become a trend, in order to be more adaptive to today's development, so that the CFG pile composite foundation bearing capacity to improve the neural network prediction comes along. In the neural network prediction, it is very important to collect and analyses the CFG pile composite foundation bearing capacity. data mining algorithms cannot accurately collect and analyse the accuracy problem in neural network prediction. As a result, this paper proposes a BP algorithm for data collection and analysis. Firstly, the indicators are divided according to the data collection requirements to reduce the interference factors in data collection. Then, the BP algorithm for neural network prediction data collection results, the formation of the data collection scheme, and the comprehensive analysis of the data collection results. MATLAB simulation shows that in the case of a certain evaluation standard, the BP algorithm for neural network prediction of the data collection accuracy, data collection reliability is better than the data mining algorithm. © 2024 IEEE.

关键词： BP algorithm CFG pile composite foundation bearing capacity data mining algorithm neural network prediction neural network prediction data collection

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Study on data mining of Time Series

Study on Data Mining of Time Series

引用

2011年信息技术、服务科学与工程管理国际学术会议

作者： Yong YE Lishui Vocational and Technical College,Zhejiang Lishui,China,323000

Time series have included a series of data materials. These data masteries are the data that is acquired through the passing of the time or the increase of the other variables. Our world is not immutable and frozen. A great amount of variables keep changing with the changes of the time. With the changes of the time,the series of the variable values have formed into a time series. As for the data mining,it is the process to abstract information and knowledge that is people have no idea with in advance but is useful in a potential manner from the data for actual application. The data for actual application here is very large,not complete,equips with noises,randomized,and obscure. This paper mainly analyses the following four matters: the research situation of the data mining of time series,the classifications of the study for data mining of the time series,the study for the data mining algorithm of sequential pattern and the analysis of the clustering of time series. The analysis is expecting to provide references to everyone.

关键词： data mining time series data mining algorithm

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Evaluation and Optimization on Applicability of Big data Technologies in Smart Grid

Evaluation and Optimization on Applicability of Big Data Tec...

引用

作者： Chenjun Sun Jing Zhou State Grid Hebei Electric Power Corporation School of Control and Computer Engineering North China Electric Power University

As a newly emerging solution to electricity problems in China, smart grid is also the development tendency of electric power industry worldwide in the future. In smart grid, huge data of electricity production involve multi-type big data technologies. Selecting suitable ones from these technologies, which will be used to analyze, compute and manage big data, is a vital to find out the potential value, law and pattern of smart ***, this paper proposes evaluation criterion and approach based on data encapsulation analysis and characteristics of big data technologies in smart grid, and finally explores the applicability of various big data technologies in smart grid.

关键词： Big data data mining algorithm Daily electricity load

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