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K-means algorithm is a classical algorithm and has been widely used in many applications. However, the traditional K-means algorithm is easily influenced by outliers and it usually obtains an unstable clustering result and poor clustering accuracy. In this paper, aiming at K-means algorithm resistant to outliers, we proposed a Capped Robust K-means Algorithm (CRK-means) by adding a capped norm and...
Fuzzy C-Means (FCM) clustering algorithm has been widely used in the field of image segmentation with its good clustering efficiency. However, it may cause a time-consuming result and even convergence to local minima because of its local search character while with an improper initial value. Therefore, an improved FCM algorithm is proposed in this paper to solve the ripe fruit image segmentation problem...
In the present, scheduling problem is a hot Cloud Computation research issues, the purpose is to coordinate the Cloud Computation resources to be fully rational use. Data locality is one of the main properties in the particular cloud platform for Hadoop. The paper discussed the property, proposed a new improvement of the Hadoop relevant data locality scheduling algorithm based on LATE. The algorithm...
Domain specific design of reconfigurable architecture is a hard and time-consuming job. In this paper, a fast and effective domain-specific design method is proposed which mainly concludes a top-down subgraph enumeration algorithm and a heuristic identification process based on topological searching. A clustering and splitting algorithm is used to enumerate all the maximal valid subgraphs (MVSs) of...
There are two shortages in usual methods for agricultural land evaluation: (1) too many manual interferences into the calculating procession, (2) the relatively large differences of partial units are concealed in certain factors. We designed a hyper graph clustering model in this paper based on fuzzy frequent item sets to conduct the evaluation for quality of agricultural land. The database for land...
Fuzzy Fisher Criterion(FFC) based clustering method uses the fuzzy Fisher's linear discriminant(FLD) as its clustering objective function and is more robust to noises and outliers than fuzzy c-means clustering(FCM). But FFC can only be used in linear separable dataset. In this paper, a novel fuzzy clustering algorithm, called Kernelized Fuzzy Fisher Criterion(KFFC) based clustering algorithm, is proposed...
This paper proposes a novel vertical format-based frequent pattern mining algorithm HBMFP. HBMFP adopts a hybrid search strategy of prefix-depth-first and depth-first searches based on the correlative array, which adequately makes use of the advantages of the both searches to effectively reduce yielded candidates as the same time keeps simplicity and lower memory cost. HBMFP uses bitmaps to store...
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