This article presents the modeling, development and theoretical grounding for the development of an application based on the clustering algorithm DBSCAN, aiming to reduce the daily waste of time on the locomotion of a huge number of people to a common place. The clusters are created based on attributes, like the departure time of each person from its residence, the final destine and its both ge...

Finding clusters with widely differing sizes, shapes and densities in presence of noise and outliers is a challenging job. The DBSCAN is a versatile clustering algorithm that can find clusters with differing sizes and shapes in databases containing noise and outliers. But it cannot find clusters based on difference in densities. We extend the DBSCAN algorithm so that it can also detect clusters...

Traditionally it had been a problem that researchers did not have access to enough spatial data to answer pressing research questions or build compelling visualizations. Today, however, the problem is often that we have too much data. Spatially redundant or approximately redundant points may refer to a single feature (plus noise) rather than many distinct spatial features. We can use density-ba...

This paper proposes a novel clustering methodology which undeniably manages to offer results with a higher inter-cluster inertia for a better clustering. The advantage obtained with this methodology is due to an algorithm that showed beforehand its efficiency in clustering exercises, MCDBSCAN, which is associated to an iterative process with a potential of auto-adjustment of the weights of the ...

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R(*)-tree (Bechmann et al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory ...

A semantics-based method for density-based clustering with constraints imposed by geographical background knowledge is proposed. In this paper, we apply an ontological approach to the DBSCAN (Density-Based Geospatial Clustering of Applications with Noise) algorithm in the form of knowledge representation for constraint clustering. When used in the process of clustering geographic information, s...

Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The amount of spatial data being collected is increasing exponentially. So, it far exceeded human’s ability to analyze. Recently, clusterin...

Malware Classification has been a challenging problem in the recent past and several researchers have attempted to solve this problem using various tools. It is security threat which can break machine operation while not knowing user’s data and it's tough to spot its behavior. This paper proposes a novel technique using DBSCAN (Density based Kmeans) algorithmic rule to spot the behavior of malw...

The advancement in digital technology and World Wide Web has increased the usage of digital documents being used for various purposes like epublishing, digital library. Increase in number of text documents requires efficient techniques that can help during searching and retrieval. Document clustering is one such technique which automatically organizes text documents into meaningful groups. This...

DBSCAN is a base algorithm for density based clustering. It can detect the clusters of different shapes and sizes from the large amount of data which contains noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. In this paper, we propose a density varied DBSCAN algorithm which is capable to handle local density variation within the cluste...