DBSCAN is widely used in various fields, but it requires computational costs similar to those of re-clustering from scratch update clusters when new data inserted. To solve this, we propose an incremental density-based clustering method that rapidly updates by identifying advance regions where cluster will occur. Also, through extensive experiments, show our provides results DBSCAN.

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape cluster...

A new density peak clustering (DPC) algorithm with adaptive center based on differential privacy was proposed to solve the problems of poor adaptability high-dimensional data, inability automatically determine centers, and in analysis. First, problem cosine distance used measure similarity between datasets. Then, aiming at subjective selection, from perspective ranking graph, weight <inline-for...

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

Finding clusters in high dimensional data is a challenging task as the high dimensional data comprises hundreds of attributes. Subspace clustering is an evolving methodology which, instead of finding clusters in the entire feature space, it aims at finding clusters in various overlapping or non-overlapping subspaces of the high dimensional dataset. Density based subspace clustering algorithms t...

We present a density based method for clustering gene expression data using a two-objective function. The method uses regulation information as well as a suitable dissimilarity measure to cluster genes into regions of higher density separated by sparser regions. The method has been tested on five benchmark microarray datasets and found to perform well in terms of homogeneity and z-score measures.

We introduce a density-based clustering method called skeleton that can detect clusters in multivariate and even high-dimensional data with irregular shapes. To bypass the curse of dimensionality, we propose surrogate density measures are less dependent on dimension but have intuitive geometric interpretations. The framework constructs concise representation given as an intermediate step be tho...