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Content-Centric Networking (CCN) proposals rethink the communication model around named data. In-network caching is a fundamental feature to distinguish the CCN from the current host-centric IP network. In this paper, we have proposed a hybrid caching scheme which combines the on-path one and the off-path one. We leverage the ISOMAP manifold learning algorithm to distinguish the importance of nodes...
In current days, data tend to become much bigger than before, and the distributed computing system is an prevalent option to deal with them. As one of powerful tools, MapReduce framework provides a cheap and efficient way to write parallel programs to run on distributed computing systems. Chance discovery (CD) is an extension of data mining, where chance refers to rare but important events or situations...
Affinity propagation clustering algorithm is with a broad value in science and engineering because of it no need to input the number of clusters in advances, robustness and good generalization. But the algorithm needs the initial similarity (the distance between any two points) as a parameter, a lot of time and storage space is required for the calculation of similarity. It's limited to apply to cluster...
In traditional grid clustering algorithms, the cluster results are just consisted of dense grids so that the clustering quality is low, while these algorithms are unable to cluster the multi-density datasets. In this paper, we propose a clustering algorithm based on grid and boundary over multi-density datasets. In order to describe the data distribution, boundary grid is introduced and checked by...
Previous studies have focused on serveral aspects of CRM (Customer Relationship Management). However, there is a lack of research that focuses on the customer segmentation of shipping enterprises using data mining. Data mining technology can be used to in modern CRM to greatly enhance it function and efficiency. Based on the technologies of clustering and classification in data mining, this paper...
Text clustering is a hot and essential topic in data mining and information retrieval. This paper proposed a KP-FCM clustering method, which used the key phrases as text features and applied the Fuzzy c-means (FCM) as clustering algorithm. In this method, key phrases were extracted by an algorithm based on suffix array. Experimental results on two standard text clustering benchmark corpuses, OHSUMED...
Clustering Web search result is a promising way to help alleviate the information overload for Web users. In this paper, we focus on clustering snippets returned by Google Scholar. We propose a novel similarity function based on mining domain knowledge and an outlier-conscious clustering algorithm. Experimental results showed improved effectiveness of the proposed approach compared with existing methods.
This paper analyses the advantages and disadvantages of the K-means algorithm and the DENCLUE algorithm. In order to realise the automation of clustering analysis and eliminate human factors, both partitioning and density-based methods were adopted, resulting in a new algorithm - Clustering Algorithm based on object Density and Direction (CADD). This paper discusses the theory and algorithm design...
This paper analyzed some problems existing in the business search engine when it searched the special fields, and then put forward a set of search engine scheme about data warehouse design based on data mining. It applied the improved association rules algorithm to text clustering algorithm. At the same time in combination with the advantages of J2EE architecture in system development, this paper...
Subspace clustering has attracted great attention due to its capability of finding salient patterns in high dimensional data. Order preserving subspace clusters have been proven to be important in high throughput gene expression analysis, since functionally related genes are often co-expressed under a set of experimental conditions. Such co-expression patterns can be represented by consistent orderings...
Finding latent patterns in high dimensional data is an important research problem with numerous applications. Existing approaches can be summarized into 3 categories: feature selection, feature transformation (or feature projection) and projected clustering. Being widely used in many applications, these methods aim to capture global patterns and are typically performed in the full feature space. In...
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