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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...
In this paper, we present a generic model to enrich user profiles by means of contextual and temporal information. This reflecting the current interests of these users in every period of time defined by a search session, and infers data freshness. We argue that the annotation of resources gives more transparency on users' needs. Based on this idea, we integrate social tagging in order to exploit part...
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...
To ascertain wheat yield losses caused by stripe rust present work deals with the development of computer intelligent system, that uses improved Apriori algorithm to extract spatial association rules from spatial databases which helps in a decision support system for wheat crop yellow rust disease management. The approach aims to extract interesting frequent patterns and spatial associations among...
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...
The important role of digital forensics is to improve the investigation of criminal activities that involve gather, to preserve, analyze, digital devices and provide technical and scientific evidence, and to provide the important documentation to authorities. To automatically group the retrieved documents into a list of meaningful categories different clustering techniques can be used. In last few...
In this paper, we propose an efficient distributed fuzzy associative classification model based on the MapReduce paradigm. The learning algorithm first mines a set of fuzzy association classification rules by employing a distributed version of a fuzzy extension of the well-known FP-Growth algorithm. Then, it prunes this set by using three purposely adapted types of pruning. We implemented the distributed...
Mining information from a database is the main aim of data mining since years. The most relevant information which one requires as a result of data mining is getting associations between various attributes. More preciously mining frequent itemset is the most significant step to initiate the mining operation. Most of the algorithms discussed in the literature require multiple scan of the database to...
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...
Discovering the hidden knowledge from large volume of educational data and applying it properly for decision making is essential for ensuring high quality education in any academic institution. This knowledge is extractable through data mining techniques. Association Rule Mining technique aims at discovering implicative tendencies that can provide valuable information for the decision maker. In this...
Frequent pattern mining has attracted a lot of attention in past twenty years because of its wide applications like commercial promotion, web search engines, and so forth. However, the execution performance suffers from the rapid growth on the database. Many of the past studies tried to use distributed computing technology to speed up the mining process, but few of them discussed how the appropriate...
We introduce a novel algorithm to detect unknown attacks, based on the Communicating Ant for Clustering (CAC) [1], which despite the other ants algorithm, lead to a better detection rate (DR). Secondly, having noted the low DR of R2L attacks, we improve this approach by hybridizing it with association rules approach. In addition to the measure of similarity calculated using continuous attributes of...
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...
For any successful launch of product, it should be properly reviewed. This is done by conducting a meeting in which various participants give their opinions, and depending upon those opinions decision is made. Firstly, it was given by Anvil tool, which was difficult to analyze. Also, commands such as propose, acknowledgement, negative response do not have a predefined notion which can differentiate...
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...
Pervasive computing is a computing method which emphasizes people oriented. This method advocates the idea that computing has to meet humans' habits. Context aware is one of core technology of pervasive computing. Association rules mining is a method that mine and detect the relation between event with another event or item from mass datasets. However, traditional association rules mining only takes...
Rapid advancement in information technology, business applications and its data storage are distributed in nature. Due to this distributed nature of the transaction databases, distributed association rule mining plays on important role to discover the interesting association and/or correlation relationships among large set of data items. Current research on distributed association rule mining focused...
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