BPF: An Effective Cluster Boundary Points Detection Technique

Khalique, Vijdan; Kitagawa, Hiroyuki

doi:10.1007/978-3-031-12423-5_31

Cited by 1 publication

(5 citation statements)

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“…To address this problem, we propose StreamBPF which employs a grid structure to efficiently compute the k-nearest neighbors and uses an incremental method of computing BPF scores. This paper is an extension of our previous work [19], and followings are the key extensions in this work:…”

Section: Introductionmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

“…Our previous work [19] introduced BPF method and experimentally showed its effectiveness for static datasets. However, in [19], we did not consider the problem to detect boundary points occurring in streaming data. To the best of our knowledge, there is no proposed method of boundary points detection for streaming data.…”

Section: Related Workmentioning

confidence: 99%

“…The problem of boundary points detection requires the detection of boundary points while ignoring the core points and outliers. In our previous work [19] , we proposed Boundary Point Factor (BPF) method which calculates a boundary point score called BPF score to effectively identify boundary points. In a nutshell, BPF calculates the BPF score of a given point by taking the ratio of its Gravity value and LOF score.…”

Section: Introductionmentioning

confidence: 99%

“…We propose BPF algorithm for static datasets which calculates BPF scores and output the top-m boundary points. Followings are the advantages of our proposed method [19]:…”

Section: Introductionmentioning

confidence: 99%

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BPF: a novel cluster boundary points detection method for static and streaming data

2023

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Data points situated near a cluster boundary are called boundary points and they can represent useful information about the process generating this data. The existing methods of boundary points detection cannot differentiate boundary points from outliers as they are affected by the presence of outliers as well as by the size and density of clusters in the dataset. Also, they require tuning of one or more parameters and prior knowledge of the number of outliers in the dataset for tuning. In this research, a boundary points detection method called BPF is proposed which can effectively differentiate boundary points from outliers and core points. BPF combines the well-known outlier detection method Local Outlier Factor (LOF) with Gravity value to calculate the BPF score. Our proposed algorithm StaticBPF can detect the top-m boundary points in the given dataset. Importantly, StaticBPF requires tuning of only one parameter i.e. the number of nearest neighbors $$(k)$$ ( k ) and can employ the same $$k$$ k used by LOF for outlier detection. This paper also extends BPF for streaming data and proposes StreamBPF. StreamBPF employs a grid structure for improving k-nearest neighbor computation and an incremental method of calculating BPF scores of a subset of data points in a sliding window over data streams. In evaluation, the accuracy of StaticBPF and the runtime efficiency of StreamBPF are evaluated on synthetic and real data where they generally performed better than their competitors.

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Section: Introductionmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…We propose BPF algorithm for static datasets which calculates BPF scores and output the top-m boundary points. Followings are the advantages of our proposed method [19]:…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations