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Current hierarchical clustering algorithms face the risk of privacy leakage during the clustering process for big dataset. While differential privacy is a relatively recent development in the field of privacy-preserving data mining, offering more robust privacy guarantees. In the paper, BIRCH algorithm under differential privacy is studied and analyzed. Firstly, Diff-BIRCH algorithm which directly...
This brief presents a framework of retargeted least squares regression (ReLSR) for multicategory classification. The core idea is to directly learn the regression targets from data other than using the traditional zero–one matrix as regression targets. The learned target matrix can guarantee a large margin constraint for the requirement of correct classification for each data point. Compared with...
Mining closed frequent item set(CFI) plays a fundamental role in many real-world data mining applications. However, memory requirement and computational cost have become the bottleneck of CFI mining algorithms, particularly when confronting with large scale datasets, which herewith makes mining closed frequent item set from large scale datasets a significant and challenging issue. To address the above...
Mining closed frequent itemset (CFI) plays an essential role in many real-world data mining applications. With the emergence of abundant large-scale data sets, it now turns to be a significant and challenging issue to mine CFI concurrently. This paper proposes a parallel balanced mining algorithm for CFI based on the MapReduce platform. The proposed algorithm adopts Greedy strategy to group items...
This paper proposes a new and practical model to achieve the electricity market equilibrium based on Linear Supply Function Equilibrium (LSFE) model. In the proposed model, the transmission constraint in addition to the generation constraint and the consumers bidding behavior are considered. The coevolutionary computational approach is proposed to solve the market equilibrium problem. Several cases...
This is a article of research which bases on the classical granular computing and the Association Rules, focus on the association rules and decision-making rules of the information system. Firstly, defines a association-rule characteristic Information Granule, which can be treated as a sub-definition of the Information Granule, and a association-rule characteristic Information Granularity matirx is...
In this paper, a novel data driven knowledge extraction scheme is proposed and applied to realize power system stability estimation since power system stability assessment can be treated as a typical classification problem (stable/unstable). The strategy is composed of three cascading layers, including the feature selection for choosing an optimal subset from candidate inputs, pattern discovery layer...
A modified pattern recognition algorithm based on the residual analysis and the recursive partition is proposed in this paper, which is more effective than the original algorithm in cases of high-dimension finite-sample practical engineering problems. The proposed approach is first verified with artificial testing data and then applied to power system transient stability assessment (TSA). Case studies...
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