Using fuzzy c-means clustering algorithm in financial health scoring

Gökten, Pınar Okan; Baser, Furkan; Gökten, Soner

doi:10.20869/auditf/2017/147/385

Cited by 15 publications

(6 citation statements)

References 61 publications

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“…S. Gokten et al also applied the method on financial health scoring applies the FCM algorithm to produce unique and sensitive financial scores. Their results show that the calculated scores are consistent with short-term price formations in terms of investor behavior and that the FCM clustering algorithm could be used to sort companies from a more sensitive perspective [34]. We use this technique to model the trajectories of mesoscale oceanic eddy.…”

Section: Results Of Anfis-fcm Models Using Both Approachesmentioning

confidence: 71%

Trajectories modelling of mesoscale anticyclonic eddies in the Mozambique Channel using ANFIS Fuzzy C-Means

2023

AIS

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The aim of this paper is to optimize the Fuzzy C-Means (FCM) model of the ANFIS neuro-fuzzy system to model the four types of mesoscale anticyclonic eddy trajectories in the Mozambique Channel as a function of the variables eddy speed average of contour, amplitude and diameter, horizontal wind, atmospheric pressure and bathymetry. The study area concerns the eastern part of the Mozambique Channel between longitudes 41°E-44°E and latitudes 16°S-25°S. We classified the eddy trajectories of interest in our study area into four types according to their formation and dissipation zones. The data used are from the mesoscale eddy track atlas product derived from the META3 altimetry version. 1exp DT allsat for trajectories and eddy properties (amplitude, eddy rotation speed and diameter), GEBCO_2022 grid data for bathymetry, ECMWF data at spatial resolution 1° x 1° for atmospheric pressure, and Copernicus Marine data at spatial resolution 0.25° x 0.25° for wind. The latitudes and longitudes of the daily eddy displacement points from their formation to their dissipation characterize the trajectories. We used two different approaches in our study. The first approach consist to put each endogenous variable as input for the FCM model, while the second approach utilized the endogenous variables multiplied by the multiple regression coefficients. The results conclude that the case where the input variables of the model are preprocessed by the multiple (linear or polynomial) regression operation before FCM modeling is the best approach.

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Section: Results Of Anfis-fcm Models Using Both Approachesmentioning

confidence: 71%

Trajectories modelling of mesoscale anticyclonic eddies in the Mozambique Channel using ANFIS Fuzzy C-Means

2023

AIS

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“…The initial 21 financial indicators are determined from the five aspects of enterprise repayment ability, profitability, operating ability, development ability, and cash flow ability (Gokten et al, 2017), with varying degrees of influence. In order to improve the efficiency and practicality of the early warning model, 21 financial indicators are analysed and researched, and as few and representative indicators as possible are selected to reflect as much information as possible, so that the selected indicators can effectively distinguish ST companies from non-ST companies.…”

Section: Experimental Datamentioning

confidence: 99%

Analysis and design of financial data mining system based on fuzzy clustering

2022

Expert Systems

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With the rapid development of the economy, a large amount of financial data will be generated during the continuous growth of enterprises. However, due to the explosive growth of the financial data range index, the use of machine learning methods to mine and analyse financial data is extremely important. Among them, accurate financial risk evaluation is an effective measure to prevent and resolve corporate financial crises. In this article, we use fuzzy clustering method to establish a financial risk early warning and evaluation model. Specifically, we use fuzzy C‐mean (FCM), half‐suppressed FCM, and interval FCM clustering algorithms‐based state construction financial risk early warning and evaluation models, to give an evaluation from two aspects of corporate financial indicators and non‐financial indicators system. In order to verify the feasibility and effectiveness of the fuzzy clustering algorithms used in financial data mining, we conducted experiments in financial data mining and early warning in real estate companies and ST companies. The experimental results show that the fuzzy clustering algorithms represented by the FCM clustering algorithm has achieved good results in financial data mining, and can achieve good results in financial risk analysis and financial risk early warning.

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“…This algorithm specifies a membership value to the items of the data for the clusters within a scope of 1 to 0. Thus an amount of fuzzy sets of partial membership can be incorporated and forms overlapping clusters for supporting it (10). This algorithm requires an argument known a fuzzification (m) which has a domain [1, n].…”

Section: Fcmmentioning

confidence: 99%

Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images

Naser

Zeki

2021

Baghdad Sci.J

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Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of the quarter that contains a tumor based on the centroid value of the cluster in this quarter, which is far from the centers of the remaining quarters. From the calculations conducted on several images' quarters, the experimental outcomes show that the centroid value of the cluster in each quarter was greater than 0.9 if this quarter did not contain a tumor while the value of the centroid value for the cluster containing a tumor was less than 0.4.For examples, in a quarter no.1 for STOMACH_1 medical image, the centroid value of the cluster was 0.973 while the value of the cluster centroid in quarter no.3 was 0.280. For this reason the tumor area was found in quarter no.(3) of the medical image STOMACH_1. Also, the centroid value of the cluster in a quarter no.2 was 0.948 for STOMACH_2 while, the value of the cluster centroid in quarter no.4 was 0.397. For this reason the tumor area was found in a quarter no.4 of the medical image STOMACH_2.

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Using fuzzy c-means clustering algorithm in financial health scoring

Cited by 15 publications

References 61 publications

Trajectories modelling of mesoscale anticyclonic eddies in the Mozambique Channel using ANFIS Fuzzy C-Means

Trajectories modelling of mesoscale anticyclonic eddies in the Mozambique Channel using ANFIS Fuzzy C-Means

Analysis and design of financial data mining system based on fuzzy clustering

Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images

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