نتایج جستجو برای: supervised clustering
تعداد نتایج: 137572 فیلتر نتایج به سال:
The k-means clustering algorithm is one of the most widely used, effective, and best understood clustering methods. However, successful use of k-means requires a carefully chosen distance measure that reflects the properties of the clustering task. Since designing this distance measure by hand is often difficult, we provide methods for training k-means using supervised data. Given training data...
Many studies in data mining have proposed a new learning called semi-Supervised. Such type of learning combines unlabeled and labeled data which are hard to obtain. However, in unsupervised methods, the only unlabeled data are used. The problem of significance and the effectiveness of semi-supervised clustering results is becoming of main importance. This paper pursues the thesis that muchgreat...
Prototype based clustering and classification algorithms constitute very intuitive and powerful machine learning tools for a variety of application areas. They combine simple training algorithms and easy interpretability by means of prototype inspection. However, the classical methods are restricted to data embedded in a real vector space and thus, have only limited applicability to complex dat...
In this paper, we define a new notion for a clustering to be useful, called actionable clustering. This notion is motivated by applications across various domains such as in business, education, public policy and healthcare. We formalize this notion by adding a novel constraint to traditional unsupervised clustering. We argue that this notion is different from semi-supervised clustering, superv...
In this paper, the authors describe and implement an algorithm to perform a supervised classification into Landsat MSS satellite images. The Maximum Likelihood Classification method is used to generate raster digital thematic maps by means of a supervised clustering. The clustering method has been proved in Landsat MSS images of different regions of Mexico to detect several training data relate...
Researches concerning (semi-)supervised clustering are recently emerging. We show two types of clustering tasks which should be axiomatically differentiated under this
Semi-supervised clustering uses a small amount of supervised information to aid unsupervised learning. As one of the semi-supervised clustering methods, metric learning has been widely used to clustering the centralized data points. However, there are many distributed data points, which cannot be centralized for the various reasons. Based on MPCK-MEANS framework [1] , the method of distributed ...
Temporal clustering refers to the partitioning of a time series into multiple nonoverlapping segments that belong to k temporal clusters, in such a way that segments in the same cluster are more similar to each other than to those in other clusters. Temporal clustering is a fundamental task in many fields, such as computer animation, computer vision, health care, and robotics. The applications ...
Semi-supervised learning, which uses a small amount of labeled data in conjunction with a large amount of unlabeled data for training, has recently attracted huge research attention due to the considerable improvement in learning accuracy. In this work, we focus on semisupervised variable weighting for clustering, which is a critical step in clustering as it is known that interesting clustering...
This paper comprehensively surveys the development of trajectory clustering. Considering the critical role of trajectory data mining in modern intelligent systems for surveillance security, abnormal behavior detection, crowd behavior analysis, and traffic control, trajectory clustering has attracted growing attention. Existing trajectory clustering methods can be grouped into three categories: ...
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