In this work we used unsupervised machine learning methods in order to find possible clustering structures superconducting materials data sets. We the SuperCon database, as well our own sets complied from literature, explore how algorithms groups superconductors. Both conventional like k-means, hierarchical or Gaussian mixtures, based on artificial neural networks self-organizing maps, were use...

Representation learning has overcome the often arduous and manual featurization of networks through (unsupervised) feature as it results in embeddings that can apply to a variety downstream tasks. The focus representation on graphs focused mainly shallow (node-centric) or deep (graph-based) approaches. While there have been approaches work homogeneous heterogeneous with multi-typed nodes edges,...

To advance the agenda in counterterrorism, this work demonstrates how analysts can combine unsupervised machine learning, exploratory data analysis, and statistical tests to discover features associated with different terrorist motives. A new empirical text mining method created a “motive” field Global Terrorism Database enable associative relationship among that characterize events. The method...

Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient scalable way to detecting these useful features, especially for high-dimensional multipartite systems. In this work, we exploit the convexity of samples without desired features design unsupervised machine learning method de...

This paper presents a novel approach to Chinese word segmentation (CWS) that attempts to utilize global information (GI) such as co-occurrence of sub-sequences and outputs of unsupervised segmentation in the whole text for further enhancement of the state-of-the-art performance of conditional random fields (CRF) learning. In the existing work of CWS, supervised and unsupervised learning seldom ...

We use Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann Machine learning rule for parameter estimation. The learning rule can be used for models with uhidden" units, or for compietely unsupervised learning. The latter is exemplified by unsupervised adaptation of an image segmentation cellular network, in particular we apply the learning rule to a...

This paper proposes an efficient similarity join method using unsupervised learning, when no labeled data is available. In our previous work, we showed that the performance of similarity join could improve when long string attributes, such as paper abstracts, movie summaries, product descriptions, and user feedback, are used under supervised learning, where a training set exists. In this work, ...

Unsupervised learning algorithms are designed to extract structure from data without reference to explicit teacher information. The quality of the learned structure is determined by a cost function which guides the learning process. This paper proposes Empirical Risk Approximation as a new induction principle for unsupervised learning. The complexity of the unsupervised learning models are auto...