Abstract
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 using context-clustering information that generates an output space through an input-output causality. Furthermore, these methods generate and use the context membership function and partition matrix information. Additionally, supervised clustering learning can obtain superior performance results for clustering, such as in classification accuracy, and prediction error. A cluster validity concept that deals with the characteristics of cluster validities and performance results in a supervised manner is considered. To show the extended possibilities of the proposed concept, it demonstrates three simulations and results in a supervised manner and analyzes the characteristics.
Highlights
Intelligent systems that optimize using learning schemes without strict mathematical constraints are a very useful approach to construct modeling in complex environments[3][4]
Clustering methods perform well in an unsupervised manner to divide input spaces and extract useful information from data sets. This helps to construct intelligent systems [5]. [10] [11] such as neural networks and fuzzy systems that divide an input space into several local spaces, in turn allowing for ease of interpretation
Context-based clustering methods [11,12,13] have used a context membership function, which was generated by a context term as output, and contained an inputoutput causality
Summary
Intelligent systems that optimize using learning schemes without strict mathematical constraints are a very useful approach to construct modeling in complex environments[3][4]. Clustering methods perform well in an unsupervised manner to divide input spaces and extract useful information from data sets This helps to construct intelligent systems [5]. Any proposed cluster validity concept can obtain more flexible criterions when it uses the input-output causality or context information such as a context membership function. This means that when the cluster validity uses more than one cluster validity result, it can attempt to induce more flexible values for the cluster validity to adapt the input-output causality, or it can introduce a performance-dependent criterion To achieve this, it proposes two combined cluster validity concepts that use the classification accuracy of a classification problem and a cluster validity of the context membership function.
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