Domain-Driven Density Based Clustering Algorithm

Antony, Neethu; Deshpande, Arti

doi:10.1007/978-981-10-0135-2_68

Cited by 1 publication

(1 citation statement)

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“…application of Euclidean distance we can define mutual distances as d i,j = ||x i − x j || 2 . Various versions of this algorithm (Antony and Deshpande 2016;Bai et al 2017) differ in the method of distance computation. The inefficient implementations (Shen et al 2016;Schubert et al 2017) calculate all mutual distances before data clustering but there are more effective procedures that rapidly decrease the time complexity of DBSCAN to O (m logm) as in the case of SLINK.…”

Section: Dbscan Techniquementioning

confidence: 99%

Concept of hidden classes in pattern classification

Hrebik

Kukal

2023

Artif Intell Rev

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Our paper presents a novel approach to pattern classification. The general disadvantage of a traditional classifier is in too different behaviour and optimal parameter settings during training on a given pattern set and the following cross-validation. We describe the term critical sensitivity, which means the lowest reached sensitivity for an individual class. This approach ensures a uniform classification quality for individual class classification. Therefore, it prevents outlier classes with terrible results. We focus on the evaluation of critical sensitivity, as a quality criterion. Our proposed classifier eliminates this disadvantage in many cases. Our aim is to present that easily formed hidden classes can significantly contribute to improving the quality of a classifier. Therefore, we decided to propose classifier will have a relatively simple structure. The proposed classifier structure consists of three layers. The first is linear, used for dimensionality reduction. The second layer serves for clustering and forms hidden classes. The third one is the output layer for optimal cluster unioning. For verification of the proposed system results, we use standard datasets. Cross-validation performed on standard datasets showed that our critical sensitivity-based classifier provides comparable sensitivity to reference classifiers.

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