A Novel Short Text Clustering Model Based on Grey System Theory

Fidan, Hüseyin; Yüksel, Mehmet Erkan

doi:10.1007/s13369-019-04191-0

Cited by 13 publications

(8 citation statements)

References 39 publications

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“…Another key-point of clustering corresponds to the data normalization (Fidan and Yuksel 2020) and data dispersion (or shape) (Kłopotek et al 2020). In a previous work, the authors of the present paper experimentally proved the importance of data preparation in terms of normalization, and the importance of the data dispersion which was qualified as space data shape; then, these two characteristics were combined with different kmeans metrics for a series of datasets.…”

Section: Introductionmentioning

confidence: 99%

Geometry-Inference based Clustering-Heuristic: An empirical method for kmeans optimal clusters determination

Khattabi

Jai

Akhrif

et al. 2022

Preprint

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Kmeans is one of the most algorithms that are utilized in data clustering. Number of metrics is coupled with kmeans in order cluster data targeting the enhancement of both locally clusters compactness and the globally clusters separation. Then, before the ultimate data assignment to their corresponding clusters, the selection of the optimal number of clusters should constitute a crucial step in the clustering process. The present work aims to build up a new clustering metric/heuristic that takes into account both space dispersion and inferential characteristics of the data to be clustered. Hence, in this paper, a Geometry-Inference based Clustering (GIC) heuristic is proposed for selecting the optimal numbers of clusters. The conceptual approach proposes the “Initial speed rate” as the main geometric parameter to be inferentially studied. After, the corresponding histograms are fitted by means of classical distributions. A clear linear behaviour regarding the distributions’ parameters was detected according to the number of optimal clusters k* for each of the 14 datasets adopted in this work. Finally, for each dataset, the optimal k* is observed to match with the change-points assigned as the intersection of two clearly salient lines. All fittings are tested using Khi2 tests showing excellent fitting in terms of p-values, and R² also for linear fittings. Then, a change-point algorithm is launched to select k*. To sum up, the GIC heuristic shows a full quantitative aspect, and is fully automated; no qualitative index or graphical techniques are used herein.

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Section: Introductionmentioning

confidence: 99%

Geometry-Inference based Clustering-Heuristic: An empirical method for kmeans optimal clusters determination

Khattabi

Jai

Akhrif

et al. 2022

Preprint

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“…Chu et al proposed a text expansion method based on short text data itself, making rich virtual documents consistent with the original documents in semantics, which are the short text clustering results' effect [40]. Fidan et al proposed a GRC model with high precision and stability in small data sets containing short texts and proved that it is an appropriate choice for short text clustering in small data sets through experiments [24]. Francesca introduced an application of ETM in brand management and proved that the whole process could reveal the characteristics of users in product preferences through the commercial application of a famous sportswear brand [41].…”

Section: Application Of Data Miningmentioning

confidence: 99%

“…There are also some text mining methods based on other languages, such as Arabic [22]. However, how to match the emotional tendency of Chinese text needs to be studied [5,[23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…Data clustering is often an initial procedure in the process of data analysis [28]. Data clustering is widely used in many fields today [24,[27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45], but most research is concentrated in the biomedical field [29]. Kmeans has been developed for more than 60 years, and is mainly used in the clustering period of data mining technology.…”

Section: Introductionmentioning

confidence: 99%

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Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Xiong

Yan

Yao³

et al. 2022

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In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and quantify the emotional orientation matching. Based on the obtained data, we design a visual interaction logic and usability test. These prove that the proposed method is feasible and effective.

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“…The grey system theory (Fidan and Yuksel 2019;Voskoglou 2018) is to analyze the known information to fully dig out the laws of the original data itself, so as to achieve an accurate description of future trends.…”

Section: Grey Clustering Theorymentioning

confidence: 99%

Bid Evaluation Method of Railway Green Construction Based on Improved PCA-Gray Variable Weight Clustering Model

bao¹,

He²

2021

Preprint

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The current bid evaluation method can no longer meet the needs of green construction of railway construction projects, so a bid evaluation method based on an improved PCA-gray variable weight clustering model is proposed. A bid evaluation index system for railway green construction projects with a hierarchical structure was constructed from three aspects: basic construction ability, business ability and green construction ability, and the index evaluation criteria were given. Considering the high dimensional complexity of the bid evaluation system, the improved PCA method was used to reduce the dimension of the bid evaluation system, and on this basis, an improved linear weighted comprehensive evaluation function was constructed to preliminarily evaluate the construction ability of bidders. Then, the grey variable weight clustering model is used to construct the definite weighted function to further analyze the green construction ability of the bidders and select the optimal bidder. Finally, the method was applied to the bid evaluation of railway engineering projects from Lanzhouxi to Zhongchuan Airport, and the conclusion is consistent with the actual bidders, which proves the applicability and effectiveness of the model.

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A Novel Short Text Clustering Model Based on Grey System Theory

Cited by 13 publications

References 39 publications

Geometry-Inference based Clustering-Heuristic: An empirical method for kmeans optimal clusters determination

Geometry-Inference based Clustering-Heuristic: An empirical method for kmeans optimal clusters determination

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Bid Evaluation Method of Railway Green Construction Based on Improved PCA-Gray Variable Weight Clustering Model

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