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• A new CDC algorithm. • 6 indicators to evaluate the results. • 26 experiments had been conducted. A variety of algorithms have recently emerged in the field of cluster analysis. Consequently, based on the distribution nature of the data, an appropriate algorithm can be chosen for the purpose of clustering. It is difficult for a user to decide a priori which algorithm would be the most appropriate for a given dataset. Algorithms based on graphs provide good results for this task. However, these algorithms are vulnerable to outliers with limited information about edges contained in the tree to split a dataset. Thus, in several fields, the need for better clustering algorithms increases and for this reason utilizing robust and dynamic algorithms to improve and simplify the whole process of data clustering has become an urgent need. In this paper, we propose a novel distance-based clustering algorithm called the critical distance clustering algorithm. This algorithm depends on the Euclidean distance between data points and some basic mathematical statistics operations. The algorithm is simple, robust, and flexible; it works with quantitative data that are real-valued, not qualitative, and categorical with different dimensions. In this work, 26 experiments are conducted using different types of real and synthetic datasets taken from different fields. The results prove that the new algorithm outperforms some popular clustering algorithms such as MST-based clustering, K-means, and Dbscan. Moreover, the algorithm can precisely produce more reasonable clusters even when the dataset contains outliers and without specifying any parameters in advance. It also provides a number of indicators to evaluate the established clusters and prove the validity of the clustering. [ABSTRACT FROM AUTHOR]

Clustering real-world data is a challenging task, since many real-data collections are characterized by an inherent sparseness and variable distribution. An appealing domain that generates such data collections is the medical care scenario where collected data include a large cardinality of patient records and a variety of medical treatments usually adopted for a given disease pathology. This paper proposes a two-phase data mining methodology ( M L C ) to iteratively analyze different dataset portions and locally identify groups of objects with common properties. Discovered cohesive clusters are then analyzed using sequential patterns to characterize temporal relationships among data features. To support an automatic classification of new data objects within one of the discovered groups, a classification model is created starting from the computed cluster set. A mobile application has been also designed and developed to visualize and update data under analysis as well as categorizing new unlabeled data objects. The experimental evaluation conducted on real datasets in the medical care scenario showed the effectiveness of M L C to discover interesting knowledge items and to easily exploit them through a mobile application. Results have been also discussed from a medical perspective. [ABSTRACT FROM AUTHOR]

Fuzzy C-means has been utilized successfully in a wide range of applications, extending the clustering capability of the K-means to datasets that are uncertain, vague and otherwise hard to cluster. This paper introduces the Fuzzy C-means++ algorithm which, by utilizing the seeding mechanism of the K-means++ algorithm, improves the effectiveness and speed of Fuzzy C-means. By careful seeding that disperses the initial cluster centers through the data space, the resulting Fuzzy C-means++ approach samples starting cluster representatives during the initialization phase. The cluster representatives are well spread in the input space, resulting in both faster convergence times and higher quality solutions. Implementations in R of standard Fuzzy C-means and Fuzzy C-means++ are evaluated on various data sets. We investigate the cluster quality and iteration count as we vary the spreading factor on a series of synthetic data sets. We run the algorithm on real world data sets and to account for the non-determinism inherent in these algorithms we record multiple runs while choosing different k parameter values. The results show that the proposed method gives significant improvement in convergence times (the number of iterations) of up to 40 (2.1 on average) times the standard on synthetic datasets and, in general, an associated lower cost function value and Xie–Beni value. A proof sketch of the logarithmically bounded expected cost function value is given. [ABSTRACT FROM AUTHOR]

The class imbalance problem occurs when one class far outnumbers the other classes, causing most traditional classifiers perform poorly on the minority classes. To tackle this problem, a plethora of techniques have been proposed, especially centered around resampling methods. This paper introduces a two-stage method that combines the DBSCAN clustering algorithm to filter noisy majority class instances with a graph-based procedure to overcome the class imbalance. We then experimentally evaluate the behavior of the proposed method on a collection of two-class imbalanced data sets. The experimental results show an improvement in the classification performance measured by the geometric mean of the accuracy on each class and also a higher reduction in the imbalance ratio when compared to several state-of-the-art under-sampling techniques. • We introduce a new under-sampling algorithm for class-imbalanced data. • This is a hybrid method based on DBSCAN clustering and a graph-based procedure. • Experiments compare the method with other state-of-the-art undersampling algorithms. • The method achieves the best classification performance and reduction in imbalance. [ABSTRACT FROM AUTHOR]

• A novel clustering algorithm proposed based on gravity center. • Uses connectivity and cohesion principles to find the center for every cluster. • Require no parameters but two coefficients to provide the flexibility. • 22 experiments were conducted in which 14 synthetic and 8 real healthcare datasets. • Better clustering quality than other hard partitional clustering algorithms. The concept of clustering is to separate clusters based on the similarity which is greater within cluster than among clusters. The similarity consists of two principles, namely, connectivity and cohesion. However, in partitional clustering, while some algorithms such as K-means and K-medians divides the dataset points according to the first principle (connectivity) based on centroid clusters without any regard to the second principle (cohesion), some others like K-medoids partially consider cohesion in addition to connectivity. This prevents to discover clusters with convex shape and results are affected negatively by outliers. In this paper a new Gravity Center Clustering (GCC) algorithm is proposed which depends on critical distance (λ) to define threshold among clusters. The algorithm falls under partition clustering and is based on gravity center which is a point within cluster that verifies both the connectivity and cohesion in determining the similarity of each point in the dataset. Therefore, the proposed algorithm deals with any shape of data better than K-means, K-medians and K-medoids. Furthermore, GCC algorithm does not need any parameters beforehand to perform clustering but can help user improving the control over clustering results and deal with overlapping and outliers providing two coefficients and an indicator. In this study, 22 experiments are conducted using different types of synthetic, and real healthcare datasets. The results show that the proposed algorithm satisfies the concept of clustering and provides great flexibility to get the optimal solution especially since clustering is considered as an optimization problem. [ABSTRACT FROM AUTHOR]