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In CBR system, the case base is becoming increasingly larger with the incremental learning which results in the decline of case retrieval efficiency and its weaker performance. Aiming at such weakness of CBR system, this article proposes a novel case retrieval method based on Hybrid Ant-Fish Clustering Algorithm (HA-FC). At beginning of algorithm, we get rough cluster sets utilizing the advantage...
As an indispensable technique in addition to the field of Information Retrieval, Ontology based Retrieval System (or Browsing Hierarchy) has been well studied and developed both in academia and industry. However, most of current systems suffer the following problems: (1) Constructing the mappings between documents and concepts in ontology requires the training of robust hierarchical classifiers; it's...
The PANDA detector is a state-of-the-art general-purpose detector for physics with high luminosity cooled antiproton beams, planed to operate at the FAIR facility in Darmstadt, Germany. The central detector includes a silicon Micro Vertex Detector (MVD) and a Straw Tube Tracker (STT) or Time Projection Chamber (TPC). The electromagnetic lead tungstate calorimeter(EMC) provides almost 4π spatial coverage,...
In the area of topic tracking, topic is developing with time. Actually speaking, traditional topic tracking approaches could track the relevant stories. However, the relation between events occurred during the topic development process could not be learned with traditional approaches. Furthermore, the whole history of the topic tracking could not be acquired either. Based on these disadvantages in...
Clustering analysis is one of the important problems in the fields of data mining and machine learning. There are many different clustering methods. Among them, k-means clustering is one of the most popular schemes owing to its simple and practicality. This paper investigates the approximate algorithm for the k-means clustering by means of selecting the k initial points from the input point set. An...
This paper presents a comparison study of the fuzzy k-means algorithm and a new variant with variable weighting in clustering high dimensional data. The fuzzy k-means algorithm is effective in discovering the clusters with overlapping boundaries. However, this effectiveness can be handicapped in high dimensional data. The recent development of the k-means algorithm with automated variable weighting...
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