DETECTION OF OUTLIERS IN TIME SERIES DATA Samson Kiware, B.A. Marquette University, 2010 This thesis presents the detection of time series outliers. The data set used in this work is provided by the GasDay Project at Marquette University, which produces mathematical models to predict the consumption of natural gas for Local Distribution Companies (LDCs). Flow with no outliers is required to dev...

In this paper we address the issue of privacy preserving clustering. Specially, we consider a scenario in which two parties owning confidential databases wish to run a clustering algorithm on the union of their databases, without revealing any unnecessary information. This problem is a specific example of secure multi-party computation and as such, can be solved using known generic protocols. H...

Clustering is the process of grouping similar data into clusters and dissimilar data into different clusters. Density-based clustering is a useful clustering approach such as DBSCAN and OPTICS. The increasing volume of data and varying size of data sets lead the clustering process challenging. So that we propose a parallel framework of clustering with advanced approach called MapReduce. We deve...

Abstract As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial of applications with noise (DBSCAN), as a common can achieve clusters via finding high-density areas separated by low-density based on cluster density. Different from other methods, DBSCAN work well for any shape the database and effectively exceptional data. However,...

Density based clustering algorithm is the primary methods for clustering in data mining. The clusters which are formed based on the density are easy to understand. It does not limit itself to the shapes of clusters. This paper gives a survey of the existing density based algorithms namely DBSCAN, VDBSCAN, DVBSCAN, ST-DBSCAN and DBCLASD based on the essential parameters needed for a good cluster...

Spatial data mining is the task of discovering knowledge from spatial data. Density-Based Spatial Clustering occupies an important position in spatial data mining task. This paper presents a detailed survey of density-based spatial clustering of data. The various algorithms are described based on DBSCAN comparing them on the basis of various attributes and different pitfalls. The advantages and...

We present NG-DBSCAN, an approximate density-based clustering algorithm that operates on arbitrary data and any symmetric distance measure. The distributed design of our algorithm makes it scalable to very large datasets; its approximate nature makes it fast, yet capable of producing high quality clustering results. We provide a detailed overview of the steps of NG-DBSCAN, together with their a...