AN EFFICIENT FUZZY BASED ANOMALY DETECTION USING COLLECTIVE CLUSTERING ALGORITHAM

Gomathi K,Umagandhi R

doi:10.26524/krj135

Abstract

Anomaly detection is a significant problem that has been researched within various research areas and application domains. Many anomaly detection methods have been particularly examined for certain application domains, as others are more standard. This present study describes an anomaly detection technique for unsupervised data sets accurately reduce the data from a kernel Eigen space performing a batch re-computation. For each anomaly behavior activities is to identify the key factors, which are used by the methods to differentiate between normal and abnormal actions. This present study provides a best and brief understanding of the techniques belonging to each anomaly and kernel mapping category. Further, for each grouping, to identify the improvements and drawbacks of the techniques in that category. It also provides a discussion on the computational complexity of the techniques since it is an important issue in real application domains hope that this survey will provide a good understanding of the many directions in which research has been done on this topic

Highlights

TO DATA MININGData Mining, “The Extraction of hidden predictive information from large databases”, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses
The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems
The proposed method called “Fuzzy based kernel mappings with adaptive Neighbouring Splitting and Merging” (FKANSM), which takes as key measures of correspondence between pairs of data points

Summary

Introduction

Data Mining, “The Extraction of hidden predictive information from large databases”, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. Data Mining consists of more no of collecting and managing data; it includes analysis and prediction. Data Mining applications can use a variety of parameters to examine the data. They include association, sequence or path analysis, classification, clustering, and forecasting. Many simpler analytical tools utilize a verification-based approach, where the user develops a hypothesis and tests the data to prove or disprove the hypothesis

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Results

Conclusion