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For the multi-index decision problem with uncertain information, this paper introduces the definition of interval distance of three-parameter interval grey number, proposes the relative degree of grey incidence based on interval distance of three-parameter interval grey number, constructs the grey incidence decision-making model with three-parameter interval grey number, measures the relative degree...
An improved canonical correlation analysis (CCA) approach for multi-subject blind source separation (BSS) of brain functional magnetic resonance imaging (fMRI) data is proposed. Group-level comparison analysis has attracted increasing interest in the human brain fMRI analysis. Canonical correlation analysis for blind source separation (BSS-CCA) relies on the fact that all meaningful real signals are...
Magnitude-only resting-state fMRI data have been largely investigated via independent component analysis (ICA) for exacting spatial maps (SMs) and time courses. However, the native complex-valued fMRI data have rarely been studied. Motivated by the significant improvements achieved by ICA of complex-valued task fMRI data than magnitude-only task fMRI data, we present an efficient method for de-noising...
The association rules mining process enables the end users to analyze, understand, and use the extracted knowledge in an intelligent system or to support the decision-making processes. To find valuable association rules from a large number of redundant rules, this paper proposes a deeper mining process, multi-mode and high value association rules mining (MH-ARM). This method takes into account the...
The problem of data deluge is prevailing everywhere. Analyzing voluminous and variety of data is a great challenge to the researchers. The MapReduce framework is adapted to many computational methodologies to overcome these issues. Clustering is one of the most commonly used data mining techniques in various pattern analysis applications. This paper is mainly focuses on quality based data clustering...
The emergence of computing power and the abundance of data have made it possible to assist human decisions, especially in the stock markets, in which the ability to predict future values would lower the risk of investing. In this paper, we present a new approach for identifying the predictive power of public emotions extracted from various sections of daily news articles on the movements of stock...
Pairwise prediction-error expansion (pairwise PEE) is an improvement of the conventional PEE and it can provide excellent performance for reversible data hiding (RDH). Unlike PEE in which the prediction-errors are modified individually, the correlation among prediction-errors is exploited in pairwise PEE by jointly modifying each prediction-error pair. In this paper, the idea of pairwise PEE is developed...
The opportunities to empirically study temporal networks nowadays are immense thanks to Internet of Things technologies along with ubiquitous and pervasive computing that allow a real-time fine-grained collection of social network data. This empowers data analytics and data scientists to reason about complex temporal phenomena, such as disease spread, residential energy consumption, political conflicts...
A recent developed band selection, called constrained band selection (CBS), makes use of constrained energy minimization (CEM) to constrain a single band to calculate its priority for band selection (BS). This paper extends such CEM-BS to a constrained multiple band selection (CMBS)-based method, to be called linearly constrained minimum variance multiple band-constrained selection (CMBS), which uses...
Mining activities has caused long-term change in land surface and hydrological cycle. Accurate information of vegetation structure is important for assessing how mining activities affect ecosystem in mining areas. A remote sensing method based on vegetation cover monitoring and assessment by using Landsat data sets with the temporal coverage from 1989 to 2015 was presented and applied to the Pingshuo...
It is a main issue to find valuable information from the power quality data because of its big volume, heterogeneity and low value density in the power quality monitoring system of the grid. An analysis system of the power quality analysis based on the data mining technologies is presented in this paper, consisting of the technologies of data cleaning, data fusion, cluster analysis, correlation analysis,...
The discovery of knowledge from the huge available data is the highest mount setback in practical pattern classification and knowledge discovery problem. The preprocessing of data plays a major role in knowledge discovery as it consequently improves the accuracy of the classifier. One of the preprocessing techniques, attribute subset selection has major importance as the selection leads to better...
Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage...
Traditional data mining approaches offers simply statistical analysis with discovery of hidden knowledge and frequent patterns. It succeeded in finding the correlation among items by statistical significance but could not provide additional parameter to knowledge discovery. In contrast to traditional approach, use of profit significance as a measure to calculate new support and confidence established...
Why are some people more creative than others? How do human brain networks evolve over time? A key stepping stone to both mysteries and many more is to compare weighted brain networks. In contrast to networks arising from other application domains, the brain network exhibits its own characteristics (e.g., high density, indistinguishability), which makes any off-the-shelf data mining algorithm as well...
With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research. They have been successfully applied to a wide range of domains, for instance, bioinformatics, speech recognition, and financial analysis. Formally speaking, given a set of data instances, a clustering algorithm is expected to divide the set of data instances into the subsets which maximize...
Study of learner-oriented mobile learning (m-learning) instructions based on classification of student m-learning strategies has aroused much attention over the last decade. Due to the multivariate nature of students' learning strategies, traditional classification methods often fail to produce reliable classification results. In this paper, a new classification method based on Principal Component...
This paper presents the development of the building electric power prediction model with local weather forecast information. Annual electric power usage data of the testbed is analyzed to develop a building electricity prediction model. K-means clustering algorithm is selected as a data mining technique. Silhouette index is applied to validate clustering results. Cluster analysis of total high voltage...
Data mining is to extract the potentially useful knowledge and information from large amounts of data. How to dig up effective, reliable, understandable, and interesting association rules from vast amounts of information to help people make decisions has become an urgent problem to be solved. People want to use a reasonable evaluation method to measure reliability or validity of association rules,...
Measurements of event related potentials in EEG are contaminated by various sources of noise including artifacts and uncorrelated spontaneous brain activities. A major problem faced during the extraction of evoked potentials is the identification and elimination of bad segments of EEG data contaminated by such sources of noise. We present a new method which automatically identifies and eliminates...
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