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Classification is the process of finding a model or function that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. The goal of classification is to accurately predict the target class for each case in the data. In sequence database having sequences, in which each sequence is a list of...
In recent years, the data mining technology has been developed rapidly. New efficient algorithms are emerging. Association data mining plays an important role in data mining, and the frequent item sets are the highest and the most costly. This paper is based on the association rules data mining technology. The advantages and disadvantages of Apriori algorithm and FP-growth algorithm are deeply analyzed...
This paper presents an approach for building annotation rules for texts containing natural language descriptions of the endoscopies, in Romanian. The annotation rule extraction relies on running the Apriori algorithm over some previously annotated texts. It does not employ any Natural Language Processing tools. We show by experiments that, for the relatively small vocabulary employed by those descriptions,...
To assess the performance of a cellular wireless network such as GSM, UMTS and LTE etc., performance counters are logged and maintained by the network management system (NMS). Due to the complexity of a network, the number of these performance counters is typically very large, and the analysis of these data is very difficult. In a typical situation, only a few number of key performance indicators...
Useful rule formation from frequent itemsets in a large database is a crucial task in Association Rule mining. Traditionally association rules refer to associations between two frequent sets and measured by their confidence. This approach basically concentrates on positive associations and thereby do not study the effect of dissociation and null transactions in association. Though the effect of dissociation...
Retail marketers are constantly looking for ways to improve the effectiveness of their campaigns. One way to do this is to target customers with the particular offers most likely to attract them back to the store and to spend more time and money on their next visit. Demographic market segmentation is an approach to segmenting markets. A company divides the larger market into groups based on several...
Extraction of interesting negative association rules from large data sets is measured as a key feature of data mining. Many researchers discovered numerous algorithms and methods to find out negative and positive association rules. From the existing approaches, the frequent pattern growth (FP-Growth) approach is well-organized and capable method for finding the item sets which are frequent, without...
A personal healthcare system used with cloud computing has been developed. It enables a daily time-series of personal health and lifestyle data to be stored in the cloud through mobile devices. The cloud automatically extracts personally useful information, such as rules and patterns concerning the user's lifestyle and health condition embedded in their personal big data, by using healthcare- data-mining...
Now a day's Data mining has a lot of e-Commerce applications. The key problem is how to find useful hidden patterns for better business applications in the retail sector. For the solution of these problems, The Apriori algorithm is one of the most popular data mining approach for finding frequent item sets from a transaction dataset and derive association rules. Rules are the discovered knowledge...
A new confabulation-inspired association rule mining (CARM) algorithm is proposed using an interestingness measure inspired by cogency. Cogency is only computed based on pairwise item conditional probability, so the proposed algorithm mines association rules by only one pass through the file. The proposed algorithm is also more efficient for dealing with infrequent items due to its cogency-inspired...
The importance of international scientific collaboration has increased, and this is most marked for China as a rising economy. This paper looks into Chinese international scientific collaboration from co-authorship at the international level by using data of ESI highly cited papers. Special attention is given to collaboration with the established economies. It first introduces the definition of association...
The goal of data mining is to help market managers find relationships among items from large data sets to increase sales volume. The Apriori algorithm is a method for association rule mining, a data mining technique. Although a lot of mining approaches have been proposed based on the Apriori algorithm, most focus on positive association rules, such as ¡§If milk is bought, then bread is bought¡¨. However,...
Most outlier detection algorithms are proposed to discover outlier patterns from static databases. Those algorithms are infeasible for instant identification of outlier patterns in data streams that continuously arriving and unbounded data serve as the data sources in many applications such as sensor data feeding. In this paper an association rules based method is proposed to find outlier patterns...
This paper introduces a fusion model to reinforce multi-level fuzzy association rules, which integrated cumulative probability distribution approach (CPDA) and multi-level taxonomy concepts to extract fuzzy association rules. The proposed model generate large item sets level by level and mine multi-level fuzzy association rule lead to finding more informative and important knowledge from transaction...
Most of the works in mining generalized association rules under fuzzy taxonomies are focused on the pre-processing stage, using the concept of extended transactions. A great problem of these transactions is related to the generation of huge amount of candidates. Beyond that, the inclusion of ancestors in database transactions ends up generating redundancy problems. Besides, it is possible to see that...
Data mining is a key technology to get useful knowledge, This paper discusses the data mining association rules' important role in online shopping, through discussing, building and analysis ing the model based on actual data, obtain that data mining association rules are playing an more and more important role in every aspects of our life and make a forecast to the future development.
Instant identification of outlier patterns is very important in modern-day engineering problems such as credit card fraud detection and network intrusion detection. Most previous studies focused on finding outliers that are hidden in numerical datasets. Unfortunately, those outlier detection methods were not directly applicable to real life transaction databases. Although a limited literature presented...
Association rule mining is sought for items through a fairly large data set relation are certainly consequential. The traditional association mining based on a uniform minimum support, either missed interesting patterns of low support or suffered from the bottleneck of item set generation. An alternative solution relies on exploiting support constraints which specifies the required minimum support...
In association rule mining, utility has recently been regarded as a practical measure for a rule's usefulness in that it can reflect the actual amount of output achieved by applying each rule. Even the same rule may have different utilities depending on how well the rule fits a specific business purpose. However, most recent studies have tried to apply a uniform standard to assessing rules disregarding...
Association rule mining is based on the assumption that users can specify the minimum-support for mining their databases. It has been identified that setting the minimum support is a difficult task to users. This can hamper the widespread applications of these algorithms. This paper proposes a method for computing minimum supports for each item. It therefore will run the fuzzy multi-level mining algorithm...
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