Stamps extraction using local adaptive k- means and ISODATA algorithms

Rajab, Maha A.; George, Loay E.

doi:10.11591/ijeecs.v21.i1.pp137-145

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…In this section, experiments on PRI-MFC, K-means [1], ISODATA [59], DBSCAN, and KMM [1] are carried out on various UCI datasets to verify the superiority of the PRI-MFC from the perspective of clustering quality, time, and algorithm parameter influence. It can be seen from the figure that the clustering effect of K-means on the tricyclic datasets, bimonthly datasets, and spiral datasets with uniform density distribution is not ideal.…”

Section: Experiments On Uci Datasetsmentioning

confidence: 99%

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An Improved Hierarchical Clustering Algorithm Based on the Idea of Population Reproduction and Fusion

Yin

Chen

2022

Electronics

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Aiming to resolve the problems of the traditional hierarchical clustering algorithm that cannot find clusters with uneven density, requires a large amount of calculation, and has low efficiency, this paper proposes an improved hierarchical clustering algorithm (referred to as PRI-MFC) based on the idea of population reproduction and fusion. It is divided into two stages: fuzzy pre-clustering and Jaccard fusion clustering. In the fuzzy pre-clustering stage, it determines the center point, uses the product of the neighborhood radius eps and the dispersion degree fog as the benchmark to divide the data, uses the Euclidean distance to determine the similarity of the two data points, and uses the membership grade to record the information of the common points in each cluster. In the Jaccard fusion clustering stage, the clusters with common points are the clusters to be fused, and the clusters whose Jaccard similarity coefficient between the clusters to be fused is greater than the fusion parameter jac are fused. The common points of the clusters whose Jaccard similarity coefficient between clusters is less than the fusion parameter jac are divided into the cluster with the largest membership grade. A variety of experiments are designed from multiple perspectives on artificial datasets and real datasets to demonstrate the superiority of the PRI-MFC algorithm in terms of clustering effect, clustering quality, and time consumption. Experiments are carried out on Chinese household financial survey data, and the clustering results that conform to the actual situation of Chinese households are obtained, which shows the practicability of this algorithm.

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Section: Experiments On Uci Datasetsmentioning

confidence: 99%

Section: Experiments On Uci Datasetsmentioning

confidence: 99%

An Improved Hierarchical Clustering Algorithm Based on the Idea of Population Reproduction and Fusion

Yin

Chen

2022

Electronics

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“…e unsupervised models are also called clustering methods, such as K-means [18,19], ISODATA [20,21], fuzzy C-means [22][23][24], and so on. e third part refers to semi-supervised learning-based models that simultaneously consider labeled and unlabeled samples in the training stage.…”

Section: Introductionmentioning

confidence: 99%

Application of Decision Tree‐Based Classification Algorithm on Content Marketing

Yang

2022

Journal of Mathematics

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Traditional content marketing methods resort grossly to market requirements but barely obtain relatively accurate marketing prediction under loads of requirements. Machine learning-based approaches nowadays are widely used in multiple fields as they involve a training process to deal with big data problems. In this paper, decision tree-based methods are introduced to the field of content marketing, and decision tree-based methods intrinsically follow the process of human decision making. Specifically, this paper considers a well-known method, called C4.5, which can deal well with continuous values. Based on four validation metrics, experimental results obtained from several machine learning-based methods indicate that the C4.5-based decision tree method has the ability to handle the content marketing dataset. The results show that the decision tree-based method can provide reasonable and accurate suggestions for content marketing.

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“…The k-means algorithm considers is the contrast of HC. Since it is one of the flat techniques [1] and treated as one of the most generally used clustering techniques for various applications [9], [10]. The idea of the kmeans clustering includes the partitioning of a given number of data N into k clusters, where k is defined in prior, such that must be k < N at the begging step in the algorithm requires initial assignment of objects into the selection of k cluster centroids so that the centroids have minimum similarity among themselves [4], [11].…”

Section: Introductionmentioning

confidence: 99%

New algorithm for clustering unlabeled big data

Mohammed

Al-Hameed

2021

IJEECS

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The clustering analysis techniques play an important role in the area of data mining. Although from existence several clustering techniques. However, it still to their tries to improve the clustering process efficiently or propose new techniques seeks to allocate objects into clusters so that two objects in the same cluster are more similar than two objects in different clusters and careful not to duplicate the same objects in different groups with the ability to cover all data as much as possible. This paper presents two directions. The first is to propose a new algorithm that coined a name (MB Algorithm) to collect unlabeled data and put them into appropriate groups. The second is the creation of a lexical sequence sentence (LCS) based on similar semantic sentences which are different from the traditional lexical word chain (LCW) based on words. The results showed that the performance of the MB algorithm has generally outperformed the two algorithms the hierarchical clustering algorithm and the K-mean algorithm.

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Stamps extraction using local adaptive k- means and ISODATA algorithms

Cited by 7 publications

References 14 publications

An Improved Hierarchical Clustering Algorithm Based on the Idea of Population Reproduction and Fusion

An Improved Hierarchical Clustering Algorithm Based on the Idea of Population Reproduction and Fusion

Application of Decision Tree‐Based Classification Algorithm on Content Marketing

New algorithm for clustering unlabeled big data

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