Papers by Keyword: Association Rule

Abstract: Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.

Abstract: Intelligent logistics is the use of integrated intelligent technology, which makes the logistics system to mimic human intelligence with the thought, perception, learning, inference and solve some problems of logistics in their ability. Association rule mining is usually more applicable and recorded in the index of discrete values. This paper analyzes theory and algorithm research of association rules data mining and presents design and development of intelligent logistics system based on data mining and association rules technology.

Abstract: Smart sensor has the following three advantages: realize the information acquisition of high precision and low cost, it relates to the micro mechanical and microelectronic, signal processing and computer technology. The purpose of data mining is to discover knowledge. Knowledge of association rules mining aims to find out the related information hidden in the database. The paper presents application analysis of smart sensor node based on data mining association technology. Experimental results show the proposed methodology has advantages in the management of the intelligent node.

Abstract: This paper studies on the data mining technology based on association rules, and analyzes on important algorithm in association rules - the advantages and disadvantages of Apriori algorithm and puts forward an improved Apriori-mapping algorithm based on address mapping. This algorithm adopts the way of horizontal deposit transaction, establishes candidate item identification list of corresponding candidate project transaction and length value of transaction list. And shorten the pruning operation time by address mapping, and compress the frequent item sets number of operation connected operation with large amplitude.The system efficiency is improved, and the performance of the algorithm has been improved by experiment.

Abstract: This paper introduces the concept of database and data mining, combined with management system of quality assessment system and method of data mining technology. In this paper, applying the data mining skill to the field of remote open management system, introduces the development of data mining in China and the necessity and importance of data mining in remote open information management system. This thesis analyzes the main problems in the remote open management system. On the basis of the relevant researches both at home and abroad, it presents the significance of the application of data mining in remote open management system. It analyzes the needs of the system based on data mining and presents a detailed design and implication of such a system.

Abstract: Most colleges and universities have built a database of student achievement, but only a simple query and statistical operations, while hiding behind the data in these achievements even more valuable information has not been excavated and use. To solve this problem, this paper proposes the use of data mining association mining method on student achievement dig deeper; get relevant information between different courses for school administrators in decision analysis, the teacher's lesson plans and student learning arrangements.

Abstract: In order to make effective use a large amount of graduate data in colleges and universities that accumulate by teaching management of work, the paper study the data mining for higher vocational graduates database using the data mining technology. Using a variety of data preprocessing methods for the original data, and the paper put forward to mining algorithm based on commonly association rule Apriori algorithm, then according to the actual needs of the design and implementation of association rule mining system, has been beneficial to the employment guidance of college teaching management decision and graduates of the mining results.

Abstract: This article put forward a NCM_Apriori algorithm, which through compressing matrix and reducing the scan times to reduce the database I/O overhead, effectively improve the efficiency of association rule mining. At the same time in the process of generating association rules, computation is greatly reduced by using the nature of probability. And applies the algorithm to the mining of students' course selection system, which can provide decision support for colleges and universities.

Abstract: According to characteristics of monitoring system, firstly, making some preliminary processing for history database through the Apriori algorithm of association rules. So that digging out some useful data for the system, and concluding the initial sample data. Secondly, analyzing, training and processing the data using artificial neural network to make the monitoring system control strategy intelligent. On the other hand, the model can be used to forecast some useful data for monitoring system, to make management of tunnel more efficient.

Abstract: The fixed assets of large enterprises are the material basis for the development of enterprises, and the innovation of financial management has great significance to the economic growth efficiency of enterprises. Only by improving the using efficiency of fixed assets and reducing the production cost, it can improve the competitiveness of large enterprises. In this paper we use the AprioriTidStr algorithm to improve the Apriori algorithm, and use VB software to establish financial management index table of fixed assets. According to the index table, we establish the search association rule knowledge base of financial management target. In order to test the stability and reliability of the system, we do numerical analysis on the fixed assets investment of enterprises, and obtain the financial forecast accurate rate curve and the financial control form within one week. It provides the computer technology support for innovation of large fixed assets management.