Clustered Comparative Analysis of Security Sensor Discrimination Data

Abstract

Abstract-Security alarm is used to protect from burglary (theft), property damage and from intruders. These security alarms consists sensors and alerting device to indicate the intrusion. Clustering is data mining technique which is used to analyzing the data. In this paper we discus about different clustering algorithm like DBSCAN, Farthest first. These algorithms are used to evaluate the different number of clusters with the sensor discrimination data base. In any organization Sensor security has many types of security alarm. It may be glass breaking alarm, smoke heat and carbon monoxide alarm, and it may be false alarm. Our aim is to compare the different algorithms with the sensors data to find density clusters i.e. which type of data will provide dense cluster of useful alarm condition. This evaluation will also detect the outliers within data such as empty alarms.

Using health data repositories for developing clinical system software: a multi‐objective fuzzy genetic approach

“…The algorithm uses a bottom‐up approach with graph theory and is less prone to initial values, but is inclined to data noise and involves complexity both in terms of space and time. The research in [29] compared the results of various clustering techniques on a heart disease prediction system. Density based spatial clustering of application with noise Algorithm (DBSCAN) showed compromised results as compare to k‐mean.…”