نتایج جستجو برای: supervised clustering
تعداد نتایج: 137572 فیلتر نتایج به سال:
This paper centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering is applied to classified examples and has the goal of identifying class-uniform clusters that have a high probability density. This paper focuses on how data mining techniques in general, and classification techniques in particular, can benefit from knowledge o...
Semi-supervised clustering is a constrained technique that organizes collection of unlabeled data into homogeneous subgroups with the help domain knowledge expressed as constraints. These methods are, most time, variants popular k-means algorithm. As such, they are based on criterion to minimize. Amongst existing semi-supervised clusterings, Evidential Clustering (SECM) deals problem uncertain/...
In this paper, a cluster validity concept from an unsupervised to a supervised manner is presented. Most cluster validity criterions were established in an unsupervised manner, although many clustering methods performed in supervised and semi-supervised environments that used context information and performance results of the model. Context-based clustering methods can divide the input spaces u...
Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved impressive performance due to the powerful representation extracted using neural networks while prioritizing categorical separability. However, self-reconstruction loss of an AE ignores rich useful relation information and might lead indiscriminative representation, which inevitably degrades performance. It is also cha...
Semi-supervised clustering is an important method which can improve clustering performance by introducing partial supervised information. This paper mainly studies the semi-supervised fuzzy clustering based on Mahalanobis distance and Gaussian Kernel for SCAPC algorithm. Here, we give a new semi-supervised fuzzy clustering objective function. By solving the optimization problem with above objec...
This work centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering assumes that the examples are classified and has the goal of identifying class-uniform clusters that have high probability densities. Three representative–based algorithms for supervised clustering are introduced: two greedy algorithms SRIDHCR and SPAM, and an e...
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