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This paper presents a new keyword extraction algorithm for Chinese news Web pages using lexical chains and word co-occurrence combined with frequency features, cohesion features, and corelation features. A lexical chain is an external performance consistency by semantically related words of a text, and is the representation of the semantic content of a portion of the text. Word co-occurrence distribution...
Unsupervised machine learning algorithms are used to perform statistical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. A clustering algorithm is used to automatically group the results of the transport and dispersion simulations according to their respective cloud characteristics. Each cluster of clouds...
Personal blogs are one of the most interconnected and socially networked type of social media. The capability of placing "comments'' on blog posts makes the blogosphere rather a complex environment.In this paper, we study the behavior of bloggers who place comments on others' posts and examine if it is possible to detect spam comments.We look at the functionality of different network motif profiles...
Recently, a new temporal dataset has been made public: it is made of a series of twelve 100 M pages snapshots of the .uk domain. The Web graphs of the twelve snapshots have been merged into a single time-aware graph that provide constant-time access to temporal information. In this paper we present the first statistical analysis performed on this graph, with the goal of checking whether the information...
If we can estimate the accuracy of our observations then we can estimate the true and false positive rates over a series of samples in high dimensional data mining problems. To date such issues have been largely neglected and previously no algorithm has been provided to facilitate the computations involved. In high dimensional data mining tasks, increasing sparsity leads to decreasing true positive...
Knowledge discovery from temporal, spatial and spatiotemporal data is critical for climate change science and climate impacts. Climate statistics is a mature area. However, recent growth in observations and model outputs, combined with the increased availability of geographical data, presents new opportunities for data miners. This paper maps climate requirements to solutions available in temporal,...
This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the hidden vector state (HVS) model. The HVS model belongs to the category of statistical learning...
The theoretical relationship between association rules and machine learning techniques needs to be studied in more depth. This article studies the use of clustering as a model for association rule mining. The clustering model is exploited to bound and estimate association rule support and confidence. We first study the efficient computation of the clustering model with K-means; we show the sufficient...
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