Titlebook: Advanced Data Mining and Applications; 16th International C Xiaochun Yang,Chang-Dong Wang,Zheng Zhang Conference proceedings 2020 Springer

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,Russell’s discovery of the ‘paradoxes’, in recent years, the existing consensus clustering approaches are mostly designed for general-purpose scenarios, yet often lack the ability to effectively and efficiently deal with high-dimensional data. To this end, this paper proposes a subspace-weighted consensus clustering approach, which is ba

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鄙视

Landon D. C. Elkind,Alexander Mugar Kleinnts from a set of points in a metric space with the smallest distance between them. This problem arises in a number of applications, such as but not limited to clustering, graph partitioning, image processing, patterns identification, and intrusion detection. Numerous algorithms have been presented

essential-fats

https://doi.org/10.1007/978-94-010-2723-6ention because they have the advantage of avoiding the combinatorial explosion of the HUI search space. Among evolutionary methods used for mining HUIs, particle swarm optimization (PSO) is the most popular. Existing PSO-based HUI mining (HUIM) algorithms transform positions according to the result

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Frauen - Männer - Geschlechterverhältnisseonal methods of text classification. Deep learning models have been proven that is able to extract features from data effectively. In this paper, we propose a deep graph convolutional network model that construct graph base on words and documents. We construct a new text graph based on the relevance

Servile

Frauen - Männer - Geschlechterverhältnisseion tasks. Although there are some popular methods in obtaining semantics, current context semantic analysis techniques, due to limited accuracy, are still a great bottleneck for text classification. This paper introduces a novel model, the densely connected Bidirectional LSTM with Max-pooling of CN

经典

https://doi.org/10.1007/978-3-658-42967-6By adapting advanced technologies, such as machine learning and deep learning, current sentence similarity computing methods mainly deal with key words and structures of sentences. The main drawback of current methods is taking no consideration of the influence of sentences context. In this paper, w

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