In many application areas, there is a need for clustering, and there is a need to take fuzzy uncertainty into account when clustering. Most existing fuzzy clustering techniques are based on the idea that an object belongs to a certain cluster if this object is close to a typical object from this cluster. In some application areas, however, this idea does not work well. One example of such application is clustering in education that is used to convert a detailed number grade into a letter grade. In such application, it is more appropriate to use clustering techniques which are based on a different idea: that an object tends to belong to the same cluster as its nearest neighbor. In this paper, we explain the relationship between this idea and...
Clustering can be defined as the process of grouping physical or abstract objects into classes of si...
This work focuses on clustering data affected by imprecision. The imprecision is managed by fuzzy se...
In this comment, we report a difficulty with the-application of the possibilistic approach to fuzzy ...
The present book outlines a new approach to possibilistic clustering in which the sought clustering ...
The application of fuzzy cluster analysis to larger data sets can cause runtime and memory overflow ...
In many practical situations, it is necessary to cluster given situations, i.e., to divide them into...
Clustering is one of the most used tools in data analysis. In the last decades, due to the increasin...
Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets ...
In many practical situations, it is necessary to cluster given situations, i.e., to divide them into...
Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has ad...
Recently, a new empirically successful algorithm was proposed for crisp clustering: the K-sets algor...
Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has ad...
Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has ad...
In this comment, we report a difficulty with the-application of the possibilistic approach to fuzzy ...
In this comment, we report a difficulty with the-application of the possibilistic approach to fuzzy ...
Clustering can be defined as the process of grouping physical or abstract objects into classes of si...
This work focuses on clustering data affected by imprecision. The imprecision is managed by fuzzy se...
In this comment, we report a difficulty with the-application of the possibilistic approach to fuzzy ...
The present book outlines a new approach to possibilistic clustering in which the sought clustering ...
The application of fuzzy cluster analysis to larger data sets can cause runtime and memory overflow ...
In many practical situations, it is necessary to cluster given situations, i.e., to divide them into...
Clustering is one of the most used tools in data analysis. In the last decades, due to the increasin...
Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets ...
In many practical situations, it is necessary to cluster given situations, i.e., to divide them into...
Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has ad...
Recently, a new empirically successful algorithm was proposed for crisp clustering: the K-sets algor...
Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has ad...
Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has ad...
In this comment, we report a difficulty with the-application of the possibilistic approach to fuzzy ...
In this comment, we report a difficulty with the-application of the possibilistic approach to fuzzy ...
Clustering can be defined as the process of grouping physical or abstract objects into classes of si...
This work focuses on clustering data affected by imprecision. The imprecision is managed by fuzzy se...
In this comment, we report a difficulty with the-application of the possibilistic approach to fuzzy ...