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Due to the limitation of memory, time complexity and data complexity, common fuzzy rule extraction algorithms can only handle small or medium data sets. Parallel computing is one of the effective tools to deal with big data. This paper mainly proposes a parallel processing method to present how to extract fuzzy if-then rules from futures trading data in the programming model MapReduce. First, the...
Most researches of time series forecasting mainly focus on the aspect of pursuing the numerical forecasting precision by constructing the quantitative model. But in the real world, precision is sometimes not necessary for perceiving and reasoning of human, and the qualitative forecasting of time series is able to satisfy requirement of some decision problems. In this paper, a new qualitative forecasting...
In this paper, the AFS fuzzy logic clustering algorithm proposed by X.D. Liu has been studied further by the improvement of the algorithm. Instead of examples of less than 10 samples in Liu's paper, we apply the improved algorithm to Wisconsin breast cancer data which has 699 samples and just the order relationships of the samples on each feature are used in the algorithm. This study shows that the...
Detecting approximate duplicate records in database is a key problem related to data quality. Given two lists of records, the duplicate detection problem consists of determining all pairs that are similar to each other, where the overall similarity between two records is defined based on domain-specific similarities over individual attributes constituting the record. In this paper, we present a synthetic...
In the framework of AFS (Axiomatic Fuzzy Sets) theory, We propose A novel weight fuzzy clustering algorithm, which is totally different from the traditional clustering algorithm based approaches. The novel weighted fuzzy clustering algorithm has three main advantages: Firstly, the procedures of the proposed algorithm are more transparent and understandable, and the clustering results not only have...
The notion of a rough set was originally proposed by Pawlak underwent a number of extensions and generalizations. Dubois and Prade (1990) introduced fuzzy rough sets which involve the use of rough sets and fuzzy sets within a single framework. Radzikowska and Kerre (2002) proposed a broad family of fuzzy rough sets, referred to as (phi, t)-fuzzy rough sets which are determined by some implication...
In this paper, first, the AFS fuzzy logic clustering algorithm has been studied further. Then, based on the fuzzy implicator, an algorithm of selecting optimal subsets of relevant features for fuzzy clustering is proposed. Thus a new AFS fuzzy logic clustering algorithm is achieved. Finally, the proposed clustering algorithm is applied to the well known real-world wine data set. Experimental results...
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