Generalized density clustering

Generalized density based clustering for spatial data mining

Improvement of density-based clustering algorithm using modifying the density definitions and input parameter

Clustering is one of the main tasks in data mining, which means grouping similar samples. In general, there is a wide variety of clustering algorithms. One of these categories is density-based clustering. Various algorithms have been proposed for this method; one of the most widely used algorithms called DBSCAN. DBSCAN can identify clusters of different shapes in the dataset and automatically i...

Rough clustering using generalized fuzzy clustering algorithm

In this paper, we present a rough k-means clustering algorithm based on minimizing the dissimilarity, which is defined in terms of the squared Euclidean distances between data points and their closest cluster centers. This approach is referred to as generalized rough fuzzy k-means (GRFKM) algorithm. The proposed method solves the divergence problem of available approaches, where the cluster cen...

Generalized Fuzzy Clustering Method

This paper presents a new hybrid fuzzy clustering method. In the proposed method, cluster prototypes are values that minimize the introduced generalized cost function. The proposed method can be considered as a generalization of fuzzy c-means (FCM) method as well as the fuzzy c-median (FCMed) clustering method. The generalization of the cluster cost function is made by applying the Lp norm. The...

Low-Noise Density Clustering

We study density-based clustering under low-noise conditions. Our framework allows for sharply defined clusters such as clusters on lower dimensional manifolds. We show that accurate clustering is possible even in high dimensions. We propose two data-based methods for choosing the bandwidth and we study the stability properties of density clusters. We show that a simple graph-based algorithm kn...