Any geographic location undergoes changes over a period of time. These changes can be observed by naked eye, only if they are huge in number spread over a small area. However, when the changes are small and spread over a large area, it is very difficult to observe or extract the changes. Presently, there are few methods available for tackling these types of problems, such as GRID, DBSCAN etc. H...

As location information of numerous Internet Thing (IoT) devices can be recognized through IoT sensor technology, the need for technology to efficiently analyze spatial data is increasing. One famous algorithms classifying dense into one cluster Density-Based Spatial Clustering Applications with Noise (DBSCAN). Existing DBSCAN research focuses on finding clusters in numeric or categorical data....

This document describes the implementation of two density-based clustering algorithms: DBSCAN [Ester1996] and SNN [Ertoz2003]. These algorithms were implemented within the context of the LOCAL project [Local2005] as part of a task that aims to create models of the geographic space (Space Models) to be used in context-aware mobile systems. Here, the role of the clustering algorithms is to identi...

This paper modifies the DBSCAN algorithm to identify fixations and saccades. This method combines advantages from dispersionbased algorithms, such as resilience to noise and intuitive fixational structure, and from velocity-based algorithms, such as the ability to deal appropriately with smooth pursuit (SP) movements.

Many security applications depend critically on clustering. However, we do not know of any clustering algorithms that were designed with an adversary in mind. An intelligent adversary may be able to use this to her advantage to subvert the security of the application. Already, adversaries use obfuscation and other techniques to alter the *representation* of their inputs in feature space to avoi...

Data warehouses provide a great deal of opportunities for performing data mining tasks such as classification and clustering. Typically, updates are collected and applied to the data warehouse periodically in a batch mode, e.g., during the night. Then, all patterns derived from the warehouse by some data mining algorithm have to be updated as well. Due to the very large size of the databases, i...

Mean shift is a nonparametric clustering technique that does not require the number of clusters in input and can find clusters of arbitrary shapes. While appealing, the performance of the mean shift algorithm is sensitive to the selection of the bandwidth, and can fail to capture the correct clustering structure when multiple modes exist in one cluster. DBSCAN is an efficient density based clus...