A hybrid approach combining generalized normal distribution optimization algorithm and fuzzy C-means with Calinski-Harabasz index for clustering optimization

In this paper, we propose a new hybrid approach, which combines Generalized Normal Distribution Optimization Algorithm (GNDOA) and fuzzy C-Means clustering (FCM). It is designed for processing unsupervised datasets. This idea target list the development about conventional function option and cluster...

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Veröffentlicht in:Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska Jg. 15; H. 3; S. 10 - 14
Hauptverfasser: Ibrahim, Moatasem Mahmood, Qasim, Omar Saber, Hussein, Talal Fadhil
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Lublin University of Technology 30.09.2025
Schlagworte:
Calinski-Harabasz index
data mining
feature selection
fuzzy C-means clustering
generalised normal distribution optimisation algorithm
ISSN:2083-0157, 2391-6761
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Abstract In this paper, we propose a new hybrid approach, which combines Generalized Normal Distribution Optimization Algorithm (GNDOA) and fuzzy C-Means clustering (FCM). It is designed for processing unsupervised datasets. This idea target list the development about conventional function option and clustering techniques. The proposed GNDOA-FCM uses normalized normal distribution concept along with FCM for more accurate and efficient clustering outputs leading to accelerated detection in survey region. Calinski-Harabasz index helps finding the number of clusters that has high compactness within each cluster and also apart from other clusters. The performance of the proposed hybrid GNDOA-FCM approach is tested extensively using different benchmark datasets. The results are compared with existing clustering methods using evaluation metrics like silhouette score & feature selection accuracy. Experimental results show that the proposed method can be flexibly set to obtain higher quality of clustering and is more effective than conventional techniques. W niniejszym artykule proponujemy nowe podejście hybrydowe, które łączy algorytm uogólnionej optymalizacji rozkładu normalnego (GNDOA) i klasteryzację rozmytych C-średnich (FCM). Zostało ono zaprojektowane do przetwarzania nienadzorowanych zbiorów danych. Pomysł ten ma na celu rozwój konwencjonalnych opcji funkcji i technik klasteryzacji. Proponowany GNDOA-FCM wykorzystuje koncepcję znormalizowanego rozkładu normalnego wraz z FCM w celu uzyskania dokładniejszych i wydajniejszych wyników klasteryzacji, co prowadzi do przyspieszenia wykrywania w badanym regionie. Wskaźnik Calińskiego-Harabasza pomaga znaleźć liczbę klastrów, które charakteryzują się wysoką zwartością w obrębie każdego klastra, a także w odniesieniu do innych klastrów. Wydajność proponowanego hybrydowego podejścia GNDOA-FCM została dokładnie przetestowana przy użyciu różnych zestawów danych benchmarkowych. Wyniki porównano z istniejącymi metodami klastrowania przy użyciu wskaźników oceny, takich jak wynik sylwetki i dokładność wyboru cech. Wyniki eksperymentów pokazują, że proponowana metoda może być elastycznie dostosowana w celu uzyskania wyższej jakości klastrowania i jest bardziej skuteczna niż konwencjonalne techniki.
AbstractList In this paper, we propose a new hybrid approach, which combines Generalized Normal Distribution Optimization Algorithm (GNDOA) and fuzzy C-Means clustering (FCM). It is designed for processing unsupervised datasets. This idea target list the development about conventional function option and clustering techniques. The proposed GNDOA-FCM uses normalized normal distribution concept along with FCM for more accurate and efficient clustering outputs leading to accelerated detection in survey region. Calinski-Harabasz index helps finding the number of clusters that has high compactness within each cluster and also apart from other clusters. The performance of the proposed hybrid GNDOA-FCM approach is tested extensively using different benchmark datasets. The results are compared with existing clustering methods using evaluation metrics like silhouette score & feature selection accuracy. Experimental results show that the proposed method can be flexibly set to obtain higher quality of clustering and is more effective than conventional techniques.
In this paper, we propose a new hybrid approach, which combines Generalized Normal Distribution Optimization Algorithm (GNDOA) and fuzzy C-Means clustering (FCM). It is designed for processing unsupervised datasets. This idea target list the development about conventional function option and clustering techniques. The proposed GNDOA-FCM uses normalized normal distribution concept along with FCM for more accurate and efficient clustering outputs leading to accelerated detection in survey region. Calinski-Harabasz index helps finding the number of clusters that has high compactness within each cluster and also apart from other clusters. The performance of the proposed hybrid GNDOA-FCM approach is tested extensively using different benchmark datasets. The results are compared with existing clustering methods using evaluation metrics like silhouette score & feature selection accuracy. Experimental results show that the proposed method can be flexibly set to obtain higher quality of clustering and is more effective than conventional techniques. W niniejszym artykule proponujemy nowe podejście hybrydowe, które łączy algorytm uogólnionej optymalizacji rozkładu normalnego (GNDOA) i klasteryzację rozmytych C-średnich (FCM). Zostało ono zaprojektowane do przetwarzania nienadzorowanych zbiorów danych. Pomysł ten ma na celu rozwój konwencjonalnych opcji funkcji i technik klasteryzacji. Proponowany GNDOA-FCM wykorzystuje koncepcję znormalizowanego rozkładu normalnego wraz z FCM w celu uzyskania dokładniejszych i wydajniejszych wyników klasteryzacji, co prowadzi do przyspieszenia wykrywania w badanym regionie. Wskaźnik Calińskiego-Harabasza pomaga znaleźć liczbę klastrów, które charakteryzują się wysoką zwartością w obrębie każdego klastra, a także w odniesieniu do innych klastrów. Wydajność proponowanego hybrydowego podejścia GNDOA-FCM została dokładnie przetestowana przy użyciu różnych zestawów danych benchmarkowych. Wyniki porównano z istniejącymi metodami klastrowania przy użyciu wskaźników oceny, takich jak wynik sylwetki i dokładność wyboru cech. Wyniki eksperymentów pokazują, że proponowana metoda może być elastycznie dostosowana w celu uzyskania wyższej jakości klastrowania i jest bardziej skuteczna niż konwencjonalne techniki.
Author Hussein, Talal Fadhil
Ibrahim, Moatasem Mahmood
Qasim, Omar Saber
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SubjectTerms Calinski-Harabasz index
data mining
feature selection
fuzzy C-means clustering
generalised normal distribution optimisation algorithm
Title A hybrid approach combining generalized normal distribution optimization algorithm and fuzzy C-means with Calinski-Harabasz index for clustering optimization
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