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Massive Open Online Courses (MOOCs) are growing substantially in numbers, and also in interest from the educational community. MOOCs are online courses aimed at large-scale interactive participation and open access via the Web that made them possible for anyone with an internet connection to enroll in free, university level courses. In this paper, we propose a novel method to discover various types...
We present an automatic approach for discovering location names in WWW data culled from diverse domains. Our approach builds upon the Apache Tika, Apache OpenNLP, and Apache Lucene frameworks. Tika is used to extract text and metadata from any file. The text and metadata are provided to Apache OpenNLP and its location entity extraction model. The discovered location entities are then delivered to...
Exploratory search is cumbersome with today's search engines, where a user aims to better understand complex concepts. Query expansions techniques have been widely used in exploratory search. However, query expansions often recommend queries that differ from the user's search intentions due to different contexts. Yet, many of users' needs could be addressed by asking people via popular Community Question...
With the rapid development of the society of China, a large number of land problems such as unused land or inefficient used land for construction exist in the process of land usages, which leads to a waste of massive land resources. In the management of land resources, not only solving the existing problems of the land, but also prediction of the problems of the land and prevention on land abuse are...
Mining important persons is significant to computer network and security, especially researches on email network centralization nowadays. Traditional PageRank algorithm is sensitive to the network disturbance because it distributes PR values evenly. This paper proposes a method which decomposes email network into different layers based on the core number, eliminates the interferential nodes from outer...
Endmember variability associated with impervious layer has been a serious problem in spectral mixture analysis (SMA). A reliable spectral library which ideally models the endmember variability is required for precise SMA. Even though many endmember bundles extraction algorithms have been proposed, there are still some problems in these methods which blur the threshold and endmember numbers. In this...
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...
This paper presents an application of educational data mining to predict undergraduate retention. The research provides valuable insight about data feature ranking, algorithm selections and validation methods based on unique types of data that come from educational settings. The data from a cohort of 972 students enrolled in 2008 at Embry-Riddle Aeronautical University (ERAU) were used to train and...
Crop phenology is the study of the timing of key natural phenomena of crops which were induced by natural or human factors. Advances in earth observation techniques have provided us a good tool to investigate the spatio - temporal patterns of crop phenology on a large scale. In this paper 16-day time-series MODIS data from 2001 to 2015 were used to exploer the information of crop phenology in eastern...
An approach based on decision rules algorithm and multi-features was proposed for desertification monitoring in Turpan Oasis using SPOT images. At first, the common methods such as principal component transformation, tasseled cap transformation and minimum noise fraction transformation were used to extract spectral feature, and vegetation index as well as wetness index were also calculated. Elevation...
Thresholding on coherence is a common practice for identifying the surface scatterers that are less affected by decorrelation noise during post-processing and visualisation of the results from multi-temporal InSAR techniques. Simple selection of the points with coherence greater than a specific value is, however, challenged by the presence of spatial dependence among observations. If the discrepancies...
Customer analysis problem is often involved in high-dimensional data analysis. In addition, the duplicates often disrupt the cluster analysis. This paper focuses on the customer analysis of an educational organization by clustering. Eliminating duplicates is implemented to improve the clustering result.
Long-term vegetation monitoring is important for the assessment of the impacts of mining and reclamation activities in an area like the Antaibao, where extensive and rapid opencast mining occur continuously. Accurate information of vegetation fraction is important for assessing how mining activities affect the ecosystem in mining areas. A remote sensing method based on vegetation cover monitoring...
Spatial co-location patterns represent the subsets of Boolean spatial features, and the instances of the pattern are frequently located together in a geographic space. Most existing co-location pattern mining methods mainly focus on whether spatial feature instances are frequently located together. However, that the occurrence of neighbor relationships is in the whole space or local area is not considered...
Mining spatial co-location pattern is one of the most important researches in the field of spatial data mining. In the past researches, many spatial co-location pattern mining algorithms and the expansions about these algorithms have been proposed. However, some of these methods often produce a large number of patterns which are difficult to use. If we want to use the subset of the prevalent co-location...
In the era of digital information, the size of data collection has been growing significantly. Knowledge results in term of association rules obtained from the set of data are numerous and hard to select. This paper proposes the approach for selecting the interesting subsets of association rules from big association results. The selective criterion is based on well-known interesting measures including...
Spatial co-locations represent the subsets of spatial features which are frequently located together in a geographic space. Spatial co-location mining has been a research hot in recent years. But the research on causal rule discovery hidden in spatial co-locations has not been reported. Maybe the features in a co-location accidentally share the similar environment, and maybe they are competitively...
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,...
In view of the current problems of system evaluation and decision support, an advanced theory of evaluation based on data station was proposed. The system effectiveness from different aspects was analyzed. For massive and non-structural characteristics a model of simulation data refinement and reconstruction processing was constructed, in order to meet different demands for system analysis a data...
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