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No Evidence of Disease (NED) is breast cancer patient condition status which it indicates that they can life, no find the cancer by tested, and without any symptoms of cancer in period of times, after they received primary treatment. NED is a critical status, because it involves the treatment type and patient cancer condition factors. This paper examines about breast cancer problem in data mining...
Classical algorithms of keywords extraction can hardly get low computational complexity and high accuracy. The association rule mining based algorithm is proposed in this paper. This algorithm adopts improved FP-Growth algorithm to extract word co-occurrence information, utilizes the similarity algorithm to eliminate synonyms, and removes noisy words and simplified features of candidates, thus reducing...
This work attempt to developing the FP-Growth data mining algorithm through use several knowledge constructions to build up a novel tool called Frequency Pattern-Knowledge Constructions (FP-KC) to find the association rules and to satisfy the goal of dimension reduction methods is using the correlation structure among the predicator variables by reduction the main three dimensions (features, samples...
Intelligent tutoring system (ITS) creates a new teaching mode, but most ITS are merely e-learning platforms that provide course study, without considering learning processes of learners, which can't effectively help learners to consolidate and review the unmastered knowledge points. Data mining techniques can extract the potential, valuable pattern or regulation from a great quantity of data. An intelligent...
Specification mining is a machine learning approach for discovering specifications of the protocols that code must obey when interacting with an application program interface or abstract data type. Two major concerns in engineering software systems are high maintenance costs and reliability of systems. To reduce maintenance efforts, there is a need for automated tools to help software developers understand...
With the advancement of their information technology, many enterprises have accumulated a large amount of business data. We hope to analyze these data on a higher level in order to use then better. The current database systems are unable to find the association rules in data, and cannot predict the developing trend and lack method mining information and knowledge hidden behind the data information...
This paper is initiated from the observation of existing research work which is related in frequent Item Set mining algorithms such as MAFIA, FP -Growth, Transaction Mapping (TM) and ECLAT(Equivalence CLAss Transformation). As per the study of above mentioned algorithms all the items are counted then its maximal sets are reordered separately. The algorithms are executed with the limitation of candidate...
In this paper, we propose a new mining of frequent itemsets algorithm, called SFI-mine algorithm. The SFI-mine constructs pattern-base by using a new method which is different from the conditional pattern-base in FP-growth, mines frequent itemsets with a new combination method without recursive construction of conditional FP-trees. It obtains complete and correct frequent itemsets. We have conducted...
Association rule mining is to find association relationships among large data sets. Mining frequent patterns is an important aspect in association rule mining. In this paper, an efficient algorithm named apriori-growth based on apriori algorithm and the FP-tree structure is presented to mine frequent patterns. The advantage of the apriori-growth algorithm is that it doesn't need to generate conditional...
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