Cluster analysis for anomaly detection in accounting

In today's world security is an aspect which is given higher priority by all political and government worldwide and aiming to reduce crime incidence. As data mining is the appropriate field to apply on high volume crime dataset and knowledge gained from data mining approaches will be useful and support police force. So In this paper crime analysis is done by performing k-means clustering on crime dataset using rapid miner tool.

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A Novelty Detection Algorithm for Multiple Normal Classes : Application to TFT-LCD Processes

Joo¹,

Kim²

2013

Journal of Korean Institute of Industrial Engineers

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Novelty detection (ND) is an effective technique that can be used to determine whether a future observation is normal or not. In the present study we propose a novelty detection algorithm that can handle a situation where the distributions of target (normal) observations are inhomogeneous. A simulation study and a real case with the TFT-LCD process demonstrated the effectiveness and usefulness of the proposed algorithm. †

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Cluster analysis for anomaly detection in accounting

Cited by 3 publications

References 25 publications

Machine Learning for Predicting the Procurement of an Audit at Small Private Banks: Is the Decision to Procure an Audit Systematic?

Machine Learning for Predicting the Procurement of an Audit at Small Private Banks: Is the Decision to Procure an Audit Systematic?

Crime Analysis using K-Means Clustering

A Novelty Detection Algorithm for Multiple Normal Classes : Application to TFT-LCD Processes

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