Clustered Comparative Analysis of Security Sensor Discrimination Data

• Autorzy: Bhargava N. , Jain A. , Kumar A. , Rath P. S. , Bansal A.

Identyfikatory

DOI

10.15439/2017KM21

• Konferencja: International Conference on Information Technology and Knowledge Management (1 ; 22-23.12.2017 ; New Delhi, India)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

acoustic; nuisance; detector; algorithm; farthest first algorithm

PL

akustyka; niedogodność; detektor; algorytm; najdalszy pierwszy algorytm

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2018
• Tom: Vol. 14
• Strony: 29--33
• Opis fizyczny: Bibliogr. 17 poz., wykr.

Twórcy

autor

Bhargava N.

• School of system sc. &engg., MDS University Ajmer, India

autor

Jain A.

• MDS University Ajmer, India

autor

Kumar A.

• Computer Science Department ACERC, Ajmer

autor

Rath P. S.

• Computer Science Department ACERC, Ajmer

autor

Bansal A.

• MDS University Ajmer, India

Bibliografia

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Uwagi

1. Preface

2. Technical Session: First International Conference on Information Technology and Knowledge Management