Name
Abstract | ||
|---|---|---|
Anomaly detection algorithms face several challenges, including processing speed, adapting to changes in dynamic environments, and dealing with noise in data. In this paper, a two-layer cluster-based anomaly detection structure is presented which is fast, noise-resilient and incremental. The proposed structure comprises three main steps. In the first step, the data are clustered. The second step is to represent each cluster in a way that enables the model to classify new instances. The Summarization based on Gaussian Mixture Model (SGMM) proposed in this paper represents each cluster as a GMM. In the third step, a two-layer structure efficiently updates clusters using GMM representation, while detecting and ignoring redundant instances. A new approach, called Collective Probabilistic Labeling (CPL) is presented to update clusters incrementally. This approach makes the updating phase noise-resistant and fast. An important step in the updating is the merging of new clusters with existing ones. To this end, a new distance measure is proposed, which is a modified Kullback–Leibler distance between two GMMs. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ins.2017.11.023 | Information Sciences |
Keywords | Field | DocType |
Anomaly detection,Incremental clustering,Noise resilience,Gaussian mixture model | Data mining,Cluster (physics),Anomaly detection,Artificial intelligence,Probabilistic logic,Merge (version control),Automatic summarization,Pattern recognition,Support vector machine,Constant false alarm rate,Machine learning,Mathematics,Mixture model | Journal |
Volume | Issue | ISSN |
429 | C | 0020-0255 |
Citations | PageRank | References |
7 | 0.46 | 69 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elnaz Bigdeli | 1 | 21 | 4.44 |
| Mehdi Mohammadi | 2 | 1091 | 50.02 |
| Bijan Raahemi | 3 | 155 | 22.29 |
| Stan Matwin | 4 | 3025 | 344.20 |