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Audio service system and enterprise HRM deployment based on decision tree algorithm and network optimization

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年 1-9页

作者： Zhao, Yuxuan Changzhou Coll Informat Technol Human Resources Dept Changzhou 213164 Jiangsu Peoples R China

The development speed of modern society is getting faster and faster, and the accumulation of information and data is also increasing, so it is necessary to provide targeted information. The existing research has solved this problem well. This paper studies the relevant literature, introduces the classification algorithm in data mining in detail, studies the existing data mining algorithm from different angles, as well as their shortcomings in practical application. The incremental learning method is used to extract data in the mining algorithm, and a decision tree algorithm is proposed to solve the problem of the classical incremental decision tree, and the experimental data is analyzed. In addition, small and medium-sized enterprises are facing problems such as financial difficulties and insufficient human resources. This paper uses the network to realize the emergency configuration section of human resources, and optimizes its configuration process through the network. According to the relevant theories of the network and the special nature of human resources, a human resource grid deployment model is proposed. Using EDA tool, a low cost audio simulation system is designed and implemented, and the circuit synthesis is successfully realized. By improving the mining algorithm, exploring the audio service system, and putting forward suggestions for the deployment of human resources in enterprises, the research results of this paper have important practical application value.

关键词： Fuzzy data mining algorithm Audio service system Enterprise human resource deployment

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Fuzzy comprehensive evaluation of physical education based on high dimensional data mining

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第3期35卷 3065-3076页

作者： Wang, Zhihui Inner Mongolia Univ Nationalities Coll Phys Educ Tongliao 028043 Peoples R China

The spectrum cluster algorithm is opposite in the other cluster algorithm has the obvious superiority that can distinguish the non-raised distribution the cluster, suits extremely in many actual problems. With this prior model this paper combines the fuzzy set system to propose the new data mining algorithm. At the same time, we build a fuzzy comprehensive evaluation model and analyze the evaluation system of physical education. The result shows that the fuzzy comprehensive evaluation method is improved by using the grey relational grade, which avoids the disadvantages of adopting the principle of maximum membership degree. Therefore, this evaluation method can be used in the evaluation of public sports teaching in colleges and universities.

关键词： mining algorithm grey system public physical education high dimensional data fuzzy comprehensive evaluation

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RETRACTED: Analysis of the Construction of Accounting Information Processing Mode in the Era of Big Data (Retracted article. See DEC, 2022)

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WIRELESS PERSONAL COMMUNICATIONS 2018年第4期102卷 3715-3724页

作者： Zheng, Li Chongqing Ind Polytech Coll Finance & Econ Sch Chongqing Peoples R China

As an XML-based extensible business reporting language, XBRL gradually emerges in accounting transaction. Research on accounting information processing model based on XBRL has important innovation significance. This paper proposed a data mining model based on the XML hierarchical structure, which stores XBRL in X-Hive database. Data mining analysis is implemented through association rule methods. DC-Aprior data mining method based on XBRL is proposed combined with Aprior algorithm and XQuery thought of association rules. Finally, the feasibility and effectiveness of this method are further verified through experiments.

关键词： XBRL Association rules mining algorithm

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An Efficient Search Space Exploration Technique for High Utility Itemset mining

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Procedia Computer Science 2023年 218卷 937-948页

作者： Suresh B Patel Sanjay M Shah Mahendra N Patel PhD Scholar Gujarat Technological University Ahmedabad India Computer Engineering L.D.C.E Ahmedabad India Information Technology Department GEC Gandhinagar Gujarat-India-380028

Data mining techniques discover the potentially helpful pattern concealed in a vast database for the decision support system in various real-life applications. One such technique is association rule mining. It finds the pattern from the transaction database to understand customer behavior. Frequent itemset mining (FIM) identifies a set of frequently purchased items together. One key drawback of FIM is that it ignores the item's importance. An item's importance plays a pivotal role in a real-world application. Therefore, it is necessary to discover the essential itemset from the transaction database that generate the high profit called the HUIM (High-Utility Itemset mining) problem. From a transaction database, a variety of strategies can be used to find high utility itemset. The HUIM techniques that use the Utility list are recent and have better performance in terms of memory utilization and running time. The key limitation of these algorithms is performing costly utility list join operations. This paper proposes an efficient search space exploration technique to lessen the number of comparisons performed for utility list join operations. Hence, it reduces the cost of utility list join operations. In addition, the execution time of the proposed method is evaluated. Further, the same is compared with appropriate existing state-of-the-art methods. Finally, extensive experiments carried out on publicly available benchmark datasets show that the proposed support count ascending order (SCAO) based approach performs much better as oblivious to the state-of-the-art methods.

关键词： mining algorithm High utility itemset mining pattern utility-list Transaction weighted utity

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Partition-Based Frequent Closed Pattern Miner

Partition-Based Frequent Closed Pattern Miner

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International Conference on Information and Communication Technology for Sustainable Development (ICT4SD)

作者： Soni, Anu Goel, Mukta Goel, Rohit Technol Inst Text & Sci Bhiwani India

ISBN: (纸本)9789811001352;9789811001338

Frequent closed pattern (FCP) mining has been an important step in data mining research. This paper introduces an algorithm to deal with the problem of finding out (FCP) from a given set of transactions. The miner works on a parallel approach based on compact matrix division to partition the data set. To filter these subtasks two methods are adopted (1) transaction set redundancy removal method and (2) itemset redundancy removal method. mining of filtered subtasks are done separately. Consolidated result obtained from mining of these filtered independent partitions show all FCPs present in transactions.

关键词： Frequent closed pattern mining algorithm Parallel mining

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An Approach to Discovering Weighted Service Pattern 10th

An Approach to Discovering Weighted Service Pattern

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10th International Scientific Conference on Future Information Technology (FutureTech)

作者： Hwang, Jeong Hee Gu, Mi Sug Namseoul Univ Dept Comp Sci Cheonan South Korea Chungbuk Natl Univ Database BioInformat Lab Cheongju South Korea

ISBN: (纸本)9783662478950;9783662478943

It is important to provide suitable services according to user context. The previous researches are focused on service frequent pattern mining based on service history provided to user. However, service value is changing because user context varies with time. Therefore it is necessary to detect service patterns considering weight of service value. In this paper we propose a mining method by service weight based on service ontology. The method uses a weight of service significance by spatio-temporal context of user and finds out the valuable service patterns. And then the searched patterns which make a combination with existing service rule is provided to user.

关键词： mining algorithm Association rule Pattern mining

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Graph Hashing and Factorization for Fast Graph Stream Classification 13

Graph Hashing and Factorization for Fast Graph Stream Classi...

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22nd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Guo, Ting Chi, Lianhua Zhu, Xingquan Univ Technol Fac Engn & Info Tech QCIS Ctr Sydney NSW 2007 Australia

ISBN: (纸本)9781450322638

Graph stream classification concerns building learning models from continuously growing graph data, in which an essential step is to explore subgraph features to represent graphs for effective learning and classification. When representing a graph using subgraph features, all existing methods employ coarse-grained feature representation, which only considers whether or not a subgraph feature appears in the graph. In this paper. we propose a fine-grained graph factorization approach for Fast Graph Stream Classification (FGSC). Our main idea is to find a set of cliques as feature base to represent each graph as a linear combination of the base cliques. To achieve this goal, we decompose each graph into a number of cliques and select discriminative cliques to generate a transfer matrix called Clique Set Matrix (M). By using M as the base for formulating graph factorization, each graph is represented in a vector space with each element denoting the degree of the corresponding subgraph feature related to the graph, so existing supervised learning algorithms can be applied to derive learning models for graph classification.

关键词： Graph stream matrix factorization mining algorithm

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An Improved Apriori-Based Personal Recommendation algorithm for E-commerce

An Improved Apriori-Based Personal Recommendation Algorithm ...

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3rd International Conference on Pervasive Computing and Applications

作者： Hu, Zhongyi Shen, Liangzhong Chen, Shengkai Wenzhou Univ Coll Comp Sci & Engn Wenzhou Zhejiang Peoples R China

ISBN: (纸本)9781424420209

In order to improve the quality of the recommended result, the Personalized Recommendation System should identify the similarity degree of visitor's accessing behavior so as to predict customers interests. The key technology is to calculate the similar distance among different objects over either all or only a subset of the dimensions. This paper, first of all, analyses the commonly-used methods and points out their shortages, and then proposes an improved Apriori-Based Personal Recommendation algorithm for E-commerce. This algorithm considers overall the minable data source, users' Similarity Metric and K-Support Bound to get the data of those access web pages, construct a matrix model having relatively high purchasing power about customer behavior, get the similar access behavior over the all or partial property space with high efficiency, help the customer find out the merchandise he wishes to buy through the mine of the similar pattern character between latent buyer and high buyer, promote customer satisfaction and truly promote the sale achievements for the enterprise.

关键词： Personal Recommendation algorithm Improved Apriori algorithm mining algorithm User Similarity E-commerce

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3 Dimensional Frequent Closed Pattern Miner

3 Dimensional Frequent Closed Pattern Miner

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Soni, Anu Goel, Mukta Goel, Rohit TIT&S Dept CE Bhiwani India TIT&S Dept IT Bhiwani India TIT&S Dept ECE Bhiwani India

ISBN: (纸本)9781467367080

Frequent Closed Pattern mining has been an important step in data mining research. This paper introduces an algorithm to deal with problem of finding frequent closed pattern from a given 3 dimensional periodical set of data. The miner works on zero removal principle to generate generators. Generators use a tree based approach to mine frequent closed pattern (FCP). To filter each newly generated node in tree, three tests are adopted 1) SRT 2) IRT 3) TVT. Nodes which pass tests are expanded further. Results obtained from mining of all filtered independent subtrees shows all FCPs present in transactions.

关键词： frequent closed pattern mining algorithm 3 dimensional mining periodical data mining

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The construction method of knowledge discovery theory system based on cognitive

The construction method of knowledge discovery theory system...

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Pacific-Asia Conference on Circuits, Communications and System

作者： Yang, Bingru Wang, Lijun Department of Information Engineering School University of Science and Technology Beijing 100083 China

ISBN: (纸本)9781457708565

The mainstream of development in knowledge discovery is researching on new high-performance and high- scalability mining algorithm. In fact, the research of process model and inner mechanism is more important, which have got enough attention. This paper proposed a new independent knowledge discovery system framework, which combines those three elements: algorithm, model and mechanism. In essence, it derives the systematic method from the general algorithms. Experimental results and researches show that this research can resolve some realistic problems such as timely knowledge databases maintaining and so on;by exposing the essence, regularity and complexity of KD, it would react on the mainstream, and become one available route of constituting the KD theory system... © 2011 IEEE.

关键词： Accuracy algorithm design and analysis Data mining Databases Heuristic algorithms Proteins Psychology data mining independent knowledge discovery system framework knowledge discovery theory system mining algorithm timely knowledge databases

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