A local mean-based distance measure for spectral clustering

Motallebi, Hassan; Nasihatkon, Rabeeh; Jamshidi, Mina

doi:10.1007/s10044-021-01040-5

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Also, in [11] the similarity of data points is computed based on the same nearest neighbours. There are some researches in which local density-based similarity measures have been utilized to construct similarity matrices or graphs [15,16,29,30]. Local statistics behaviours of data have also been considered in some researches.…”

Section: Introductionmentioning

confidence: 99%

Jointly local statistics and topological proximity consideration in spectral clustering

Jamshidi

2023

Preprint

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Efficient deals of mathematical techniques have been devoted to cluster data with complicated manifold structures. Although local geometrical properties of data are considered in these techniques, some essential local statistics behaviours are ignored. In this paper, a method is proposed in which local topological and local statistics structures are jointly considered. More precisely, local correlations, local distributions and high-order proximity of data points are simultaneously considered to construct more efficient affinity matrix. Local correlations and high-order proximity are considered to modify the connectivity of the graph representation of data, and local distributions are utilized to obtain a free parameter similarity function between data points. After obtaining the new similarity matrix, spectral clustering is employed to evaluate the efficiency of it. The experimental results on various benchmark data sets demonstrate that the new method can significantly improve the clustering performance compared with state-of-the-art techniques.

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Section: Introductionmentioning

confidence: 99%

Jointly local statistics and topological proximity consideration in spectral clustering

Jamshidi

2023

Preprint

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“…Spectral clustering is able to prevent the uncertain interference of the randomness. It characterizes the data connection with an appropriate graph whose vertices represent the data points and the weights represent the connection between data pairs, and tries to partition the vertices into different clusters by minimizing the cut information [ 27 ]. For this consideration, we jointly perform spectral clustering in the K-means process, in the way of adjusting the spectral rotation to derive the underlying data connection.…”

Section: Introductionmentioning

confidence: 99%

Analysis of college students’ canteen consumption by broad learning clustering: A case study in Guangdong Province, China

et al. 2022

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Investigation on college students’ consumption ability help classify them as from rich or relative poor family, thus to distinguish the students who are in urgent need for government’s economic support. As canteen consumption is the main part of the expenses of the college students, we proposed the adjusted K-means clustering methods for discrimination of the college students at different economic levels. To improve the discrimination accuracy, a broad learning network architecture was built up for extracting informative features from the students’ canteen consumption records. A fuzzy transformed technique was combined in the network architecture to extend the candidate range for identifying implicit informative variables from the single type of consumption data. Then, the broad learning network model is fully trained. We specially designed to train the network parameters in an iterative tuning mode, in order to find the precise properties that reflect the consumption characteristics. The selected feature variables are further delivered to establish the adjusted K-means clustering model. For the case study, the framework of combining the broad learning network with the adjusted K-means method was applied for the discrimination of the canteen consumption data of the college students in Guangdong province, China. Results show that the most optimal broad learning architecture is structured with 14 hidden nodes, the model training and testing results are appreciating. The results indicated that the framework was feasible to classify the students into different economic levels by analyzing their canteen consumption data, so that we are able to distinguish the students who are in need for financial aid.

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An efficient clustering algorithm based on searching popularity peaks

Motallebi,

Malakoutifar

2024

Pattern Anal Applic

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A local mean-based distance measure for spectral clustering

Cited by 5 publications

References 32 publications

Jointly local statistics and topological proximity consideration in spectral clustering

Jointly local statistics and topological proximity consideration in spectral clustering

Analysis of college students’ canteen consumption by broad learning clustering: A case study in Guangdong Province, China

An efficient clustering algorithm based on searching popularity peaks

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