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Author

Enea Mançellari

Bio: Enea Mançellari is an academic researcher from Epoka University. The author has contributed to research in topics: Fuzzy clustering & Fuzzy logic. The author has an hindex of 2, co-authored 4 publications receiving 10 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper is providing a heterogeneous cluster ensemble approach to improve the stability of fuzzy cluster analysis by applying different fuzzy clustering algorithms on the datasets obtaining multiple partitions, which in the later stage will be fused into the final consensus matrix.

10 citations

Book ChapterDOI
05 Sep 2018
TL;DR: A hybrid fuzzy clustering model combining variants of fuzzy c-means clustering and density based clustering for exploring well-structured user feedback data intending to exploit the advantages of these two types of clustering approaches and diminishing their drawbacks is presented.
Abstract: In today’s dynamic environments, user feedback data are a valuable asset providing orientations about the achieved quality and possible improvements of various products or services. In this paper we will present a hybrid fuzzy clustering model combining variants of fuzzy c-means clustering and density based clustering for exploring well-structured user feedback data. Despite of the multitude of successful applications where these algorithms are applied separately, they also suffer drawbacks of various kinds. So, the FCM algorithm faces difficulties in detecting clusters of non-spherical shapes or densities and moreover it is sensitive to noise and outliers. On the other hand density-based clustering is not easily adaptable to generate fuzzy partitions. Our hybrid clustering model intertwines density-based clustering and variations of FCM intending to exploit the advantages of these two types of clustering approaches and diminishing their drawbacks. Finally we have assessed and compared our model in a real-world case study.

3 citations

Proceedings ArticleDOI
12 Nov 2019
TL;DR: Several models employing the fuzzy clustering techniques in data compression systems are demonstrated and image compression based on fuzzy transforms for compression and decompression of color videos is described in details.
Abstract: Data compression is the process of reducing the amount of necessary memory for the representation of a given piece of information. This process is of great utility especially in digital storage and transmission of the multimedia information and it typically involves various encoding/decoding schemes. In this work we will be primarily focused on some compression schemes which employ specific forms of clustering known as fuzzy clustering. In the data mining context, fuzzy clustering is a versatile tool which analyzes heterogeneous collections of data providing insights on the underlying structures involving the concept of partial membership. Several models employing the fuzzy clustering techniques in data compression systems are demonstrated and image compression based on fuzzy transforms for compression and decompression of color videos is described in details.

1 citations

Book ChapterDOI
27 Aug 2019
TL;DR: This study is providing a semi-supervised fuzzy clustering model which modifies versions of conventional DBSCAN algorithm in order to generate soft clusters which foreclose the noise points.
Abstract: In the data mining context, semi-supervised learning is applicable in circumstances where only a scarce amount of information on the intrinsic structure of a dataset is available. This information may be in the form a few labelled instances or a relatively small set of constraints on the pairwise memberships of particular instances. In this study we are providing a semi-supervised fuzzy clustering model which modifies versions of conventional DBSCAN algorithm in order to generate soft clusters which foreclose the noise points. The employed modifications are mostly related to the control parameters of the algorithm intending to utilize the additional information (which in our case is in the form of a few labelled instances) and adaptations towards the fuzzy clustering approach. Finally, several experimental procedures have been conducted on synthetic and real-world benchmark datasets in order to assess the accuracy of our employed model and to compare it to the conventional algorithms of the respective domain.

Cited by
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Journal ArticleDOI
TL;DR: A novel fuzzy clustering ensemble framework based on a new fuzzy diversity measure and a fuzzy quality measure to find the base-clusterings with the best performance and the effectiveness of the proposed approach compared to the state-of-the-art methods in terms of evaluation criteria on various standard datasets is revealed.
Abstract: In spite of some attempts at improving the quality of the clustering ensemble methods, it seems that little research has been devoted to the selection procedure within the fuzzy clustering ensemble. In addition, quality and local diversity of base-clusterings are two important factors in the selection of base-clusterings. Very few of the studies have considered these two factors together for selecting the best fuzzy base-clusterings in the ensemble. We propose a novel fuzzy clustering ensemble framework based on a new fuzzy diversity measure and a fuzzy quality measure to find the base-clusterings with the best performance. Diversity and quality are defined based on the fuzzy normalized mutual information between fuzzy base-clusterings. In our framework, the final clustering of selected base-clusterings is obtained by two types of consensus functions: (1) a fuzzy co-association matrix is constructed from the selected base-clusterings and then, a single traditional clustering such as hierarchical agglomerative clustering is applied as consensus function over the matrix to construct the final clustering. (2) a new graph based fuzzy consensus function. The time complexity of the proposed consensus function is linear in terms of the number of data-objects. Experimental results reveal the effectiveness of the proposed approach compared to the state-of-the-art methods in terms of evaluation criteria on various standard datasets.

46 citations

Journal ArticleDOI
TL;DR: Experimental results on various standard datasets demonstrated the effectiveness of the proposed approach compared to the state-of-the-art methods in terms of evaluation criteria and clustering robustness.

41 citations

Book ChapterDOI
05 Sep 2018
TL;DR: A hybrid fuzzy clustering model combining variants of fuzzy c-means clustering and density based clustering for exploring well-structured user feedback data intending to exploit the advantages of these two types of clustering approaches and diminishing their drawbacks is presented.
Abstract: In today’s dynamic environments, user feedback data are a valuable asset providing orientations about the achieved quality and possible improvements of various products or services. In this paper we will present a hybrid fuzzy clustering model combining variants of fuzzy c-means clustering and density based clustering for exploring well-structured user feedback data. Despite of the multitude of successful applications where these algorithms are applied separately, they also suffer drawbacks of various kinds. So, the FCM algorithm faces difficulties in detecting clusters of non-spherical shapes or densities and moreover it is sensitive to noise and outliers. On the other hand density-based clustering is not easily adaptable to generate fuzzy partitions. Our hybrid clustering model intertwines density-based clustering and variations of FCM intending to exploit the advantages of these two types of clustering approaches and diminishing their drawbacks. Finally we have assessed and compared our model in a real-world case study.

3 citations

Proceedings ArticleDOI
12 Nov 2019
TL;DR: Several models employing the fuzzy clustering techniques in data compression systems are demonstrated and image compression based on fuzzy transforms for compression and decompression of color videos is described in details.
Abstract: Data compression is the process of reducing the amount of necessary memory for the representation of a given piece of information. This process is of great utility especially in digital storage and transmission of the multimedia information and it typically involves various encoding/decoding schemes. In this work we will be primarily focused on some compression schemes which employ specific forms of clustering known as fuzzy clustering. In the data mining context, fuzzy clustering is a versatile tool which analyzes heterogeneous collections of data providing insights on the underlying structures involving the concept of partial membership. Several models employing the fuzzy clustering techniques in data compression systems are demonstrated and image compression based on fuzzy transforms for compression and decompression of color videos is described in details.

1 citations

Proceedings ArticleDOI
01 Dec 2019
TL;DR: A new fuzzy clustering ensemble model based on cluster forests method (CF) that can simultaneously reduce the execution time and consists mainly of two steps: generation of clusters instances and aggregation of global models.
Abstract: With the accumulation of the large data size, clustering of big data is a challenging task. However, data reduction is considered as a powerful model which significantly reduces execution time. This work presents a new fuzzy clustering ensemble model based on cluster forests method (CF) that can simultaneously reduce the execution time and consists mainly of two steps: generation of clusters instances and aggregation of global models. In the beginning, this algorithm makes multiple clusters instances using fuzzy clustering bdrFCM technique. Secondly, it aggregates this clusters to obtain final results using Ncut spectral clustering. We call it as FCE-CF approach. This proposed method is guided by cluster validity index kappa. Experimental results demonstrate that the FCE-CF outperforms the existing clustering methods in terms of time and memory on big data UCI repository.

1 citations