Fuzzy Density Peaks Clustering

Bian, Zekang; Chung, Fu-Lai; Wang, Shitong

doi:10.1109/tfuzz.2020.2985004

Cited by 34 publications

(12 citation statements)

References 47 publications

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“…After removing the unnecessary edges, each minimum spanning tree is considered a cluster. A faster LDP-MST method is proposed in Ref- [29], which is not conducive to running on high-dimensional and large-scale data.…”

Section: B Density-peak Clusteringmentioning

confidence: 99%

Online Active Learning Framework for Data Stream Classification With Density-Peaks Recognition

2023

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This paper considers a practical scenario -data stream classification, and uses online active learning (OAL) to assist the classifier. Most active learning are proposed based on label uncertainty, but the methods based on representativeness are faced with difficulties in stream scenarios. The sampling criteria based on representativeness are usually fixed, which makes it difficult for them to work effectively in dynamic sample spaces, especially for concept drift streams. This paper devises a novel OAL framework based on sample representativeness. The framework uses local nearest-neighbor relation to measure the representativeness of unlabeled samples and divides the maximum influence space of the representative sample. We also develop an independent mechanism to identify and store short-term cluster fragments to ensure the integrity of information. The framework is deployed to the online environment and adapts to any incremental learner. Simulation experiments on multiple datasets and classifiers show that our method can help primary classifier deal with data stream environment effectly. Compared with other online active learning, the method can achieve more stable accuracy and better anti-noise ability under fewer budget.

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Section: B Density-peak Clusteringmentioning

confidence: 99%

Online Active Learning Framework for Data Stream Classification With Density-Peaks Recognition

2023

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“…The density peaks for DPC are selected automatically using the scheme reported in [38]. It is challenging for DBSCAN to deal with various datasets with fixed parameters; in this work, the value of minpts is set to ⌊ln|n|⌋ as recommended in [43] and the value of eps is set to the optimal set in the pool of [1,5] with the step being valued as 0.2. For PCM, the fuzzy parameter and error are set to 1.2 and 0.001, respectively.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…C LUSTERING refers to dividing existing unlabelled data instances into a number of clusters according to the similarity between objects without any prior information, leading to high inter-cluster similarity and low intra-cluster similarity between instances. Clustering analysis typically uses a precise similarity measure to gauge the similarity between instances and then determines the division of clusters according to specific clustering strategies [1]. A broad spectrum of clustering algorithms have been developed successfully using fuzzy sets and rough sets [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Rough Intrigued Harmonic Discrepancy Clustering

Yue

Yang

et al. 2023

IEEE Trans. Fuzzy Syst.

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Fuzzy clustering decomposes data into clusters using partial memberships by exploring the cluster structure information, which demonstrates comparable performance for knowledge exploitation under the circumstance of information incompleteness. In general, this scheme considers the memberships of objects to cluster centroids and applies to clusters with the spherical distribution. In addition, the noises and outliers may significantly influence the clustering process; a common mitigation measure is the application of separate noise processing algorithms, but this usually introduces multiple parameters which are challenging to be determined for different data types. This paper proposes a new fuzzy-rough intrigued harmonic discrepancy clustering (HDC) algorithm by noting that fuzzy-rough sets offer a higher degree of uncertainty modelling for both vagueness and imprecision present in real-valued datasets. The HDC is implemented by introducing a novel concept of harmonic discrepancy, which effectively indicates the dissimilarity between a data instance and foreign clusters with their distributions fully considered. The proposed HDC is thus featured by a powerful processing ability on complex data distribution leading to enhanced clustering performance, particularly on noisy datasets, without the use of explicit noise handling parameters. The experimental results confirm the effectiveness of the proposed HDC, which generally outperforms the popular representative clustering algorithms on both synthetic and benchmark datasets, demonstrating the superiority of the proposed algorithm.

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“…Euclidean distance, are not suitable for the clusters with arbitrary shapes and sizes in the space, such as nonspherical clusters [15] or imbalanced clusters [1], [4]. A few improved methods have been developed [1], [16]- [17]. For example, the literature [1] presents a multi-center (MC) clustering method to avoid the "uniform effect" of imbalanced data 1 .…”

Section: Introductionmentioning

confidence: 99%