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For a large sum of data collected and stored continually, it is more and more necessary to mine association rules from database, and the Apriori algorithm of association rules mining is the most classical algorithm of database mining and is widely used. However, Apriori algorithm has some disadvantages such as low efficiency of candidate item sets and scanning data frequently. Support and confidence...
Globally the internet is been accessed by enormous people within their restricted domains. When the client and server exchange messages among each other, there is an activity that can be observed in log files. Log files give a detailed description of the activities that occur in a network that shows the IP address, login and logout durations, the user's behavior etc. There are several types of attacks...
Frequent itemset mining is the technique used mostly in field of data mining like finance, health care system. We are focusing on methodologies for extracting the useful knowledge from given data by using frequent itemset mining. Most important use of FIM is customer segmentation in marketing, shopping cart analyzes, management relationship, web usage mining, and player tracking and so on. Association...
Sequential pattern mining is valuable approach to uncover consumer buying behaviour from huge sequence database. Weather prediction, web log analysis, stock market analysis, scientific research, sales analysis, and so on are the application of sequential pattern mining. The pattern that is recent and profitable can't discover by conventional sequential pattern mining. So, RFM-based sequential pattern...
Using the application of mathematical statistics analysis theory, this paper presents a related data mining analysis model based on the Pearson's r. We introduced Pearson's r to mine association rules of distinctively related courses. Thus we build the computer aided teaching evaluation system, and then draw a useful conclusion for teaching.
XML actually developed as a benchmark for caching, dispense and interchanging data over multiple platforms. The XML data is on the grow over the time in fast rate. Enterprises want formulating queries on XML datasets habitually. As giant XML data is retrievable, it is not easy job to pull out vital data from XML dataset. It is computationally expensive to answer queries without any sustain. Towards...
Mining the frequent itemsets are still one of the data mining research challenges. Frequent itemsets generation produce extremely large numbers of generated itemsets that make the algorithms inefficient. The reason is that the most traditional approaches adopt an iterative strategy to discover the itemsets, that's require very large process. Furthermore, the present mining algorithms cannot perform...
Association rule mining (ARM) is a well-researched domain in the field of data mining. It is seen as a problem of predicting customers purchasing behavior, popularly known as “Market Basket Analysis”. This problem can be solved by using Apriori algorithm which is majorly 3-steps (Joining, Pruning and Verification) process. In this paper, an alternate to Apriori algorithm's pruning step is proposed...
Association rules can mine the relevant evidence of computer crime from the massive data and association rules among data itemset, and further mine crime trends and connections among different crimes. They can help polices detect case and prevent crime with clues and criterions. Frequent itemset mining (FIM) plays a fundamental role in mining associations, correlations and many real-world data mining...
In recent years, data mining has become a global research area for acquiring interesting relationships hidden in large data sets. Data Mining has been used in various application domains such as market basket data, bioinformatics, medical diagnosis, web mining and scientific data analysis. In this paper, we have tried to optimize the rules generated by Association Rule Mining using Biogeography Based...
Frequent Itemset [1] mining is a key step in association rule problem. Early classical algorithms are serial algorithms, such as the Apriori [2], and FP-growth [3]. With the rapid development of information technology, today, the amount of data often needed to handle is based on GB or TB, which has forced the efficiency of mining algorithms significantly improved. At present, more effective method...
Nowadays, as information system plays critical part in the internet, the importance of secure networks is tremendously increased. Intrusion Detection System (IDS) is used to preserve the data integrity, confidentiality and system availability from attacks. Data mining is used to clean, classify and examine large amount of network data. Since a large volume of network traffic that requires processing,...
In this paper, Data Mining is introduced into the Intrusion Detection System, which overcomes the defects of traditional detection technology. The nuclear association rules algorithm applied to the intrusion detection matrix is optimized, which make it possible to reduce the Average-Case Time Complexity, improve the efficiency considerably, and make it easy to process magnanimity data. In this way,...
Aiming at the problem of low efficiencies for frequent item sets mining based on FP-Tree, which need a great lot of recursion call. This paper proposes another mining algorithm which uses a improved data structure named node linked list FP-Tree (NLFP-Tree). NLFP-Tree compresses the FP-Tree greatly by record the node's prefix path using node linked list. And mines frequent item sets by bottom-to-up...
Today's fast developing modern information technology not only has a great impact on the social and economic activities but also more importantly has caused the innovation of modern auditing technology. In order to keep pace with the development of modern audit, the author adopted the method of computer data mining to analyze large quantities of data collected from the audited corporate, and work...
The article presents a new algorithm for mining weighted frequent itemsets without generating candidate, which based on weighted Fp-tree and the weighted model proposed by Feng Tao. For solving the problem that the weighed support may be bigger than 1, the weight set of attributes was normalized. The new algorithm is testified to satisfy weighted downward closure property and an effectively mining...
Computer logs are generated by application activities, network accesses and system audit, which are important data sources for user pattern mining, computer forensic analysis, intrusion detection analysis and outlier detection. Algorithms for mining association rule are useful methods to find interesting rules implied in large computer log data. But existing algorithms which based on confidence and...
Low support makes dramatic increase in the number of itemsets and brings less efficient frequent itemset mining. Correlation measures introduced to restrict the number of frequent itemsets generated in order to improve the efficiency of mining under certain conditions. An improved FP-Tree algorithm using node linked list FP-Tree is proposed. This algorithm exploits efficient pruning strategies using...
User requirements obtained through text data mining are very important to improve the competitiveness of enterprises. In this paper an algorithm of acquiring user requirements in machinery products by using text association rule is proposed. In the algorithm, the user requirement documents are represented by vector space model. The feature words matrix is obtained by transposing the documents matrix...
In mining association rules, Item sets with high-length usually has lower support, but still have potential value. To mine efficacious association rules under long-pattern, a new mining method of efficacious association rules is proposed under length-decreasing support constraint. Compare to other mining methods of association rules, the new method can mine more efficacious long-patterns and improve...
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