One of the most important applications of hyperspectral data analysis is either supervised or unsupervised classification for land cover mapping. Among different unsupervised methods, Partitional Clustering has attracted a lot of attention, due to its performance and efficient computational time. The success of Partitional Clustering of hyperspectral data is, indeed, a function of five parameters: 1) the number of clusters, 2) the position of clusters, 3) the number of bands, 4) the spectral position of bands, and 5) the similarity measure. As a result, Partitional Clustering can be considered as an optimization problem whose goal is to find the optimal values for above-mentioned parameters. Depending on this fact that which of these five parameters entered to the optimization four different scenarios have been considered in this paper to be resolved by particle swarm optimization. Our goal is, then, finding the solution leading to the best accuracy. It should be noted that among five different parameters of Clustering, both similarity measure and the number of clusters have been considered fixed to prevent over-parameterization phenomenon. Investigations on a simulated dataset and two real hyperspectral data showed that the case in which the number of bands has been reduced in a pre-processing stage using either band Clustering in the data space or PCA in the feature space, can result in the highest accuracy and efficiency for thematic mapping.

Imperialist Competitive Algorithm (ICA) is considered as prime meta-heuristic algorithm to find the general optimal solution in optimization problems. This paper presents a use of ICA for automatic Clustering of huge unlabeled data sets. By using proper structure for each of the chromosomes and the ICA، at run time، the suggested method (ACICA) finds the optimum number of clusters while optimal Clustering of the data simultaneously. To increase the accuracy and speed of convergence، the structure of ICA changes. The proposed algorithm requires no background knowledge to classify the data. In addition، the proposed method is more accurate in comparison with other Clustering methods based on evolutionary algorithms. DB and CS cluster validity measurements are used as the objective function. To demonstrate the superiority of the proposed method، the average of fitness function and the number of clusters determined by the proposed method is compared with three automatic Clustering algorithms based on evolutionary algorithms.

Distance-based Clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based Clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by adaptively updating the parameters during Clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the Clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known Clustering methods.

Traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. To eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. Fortunately, geospatial data is considered a proper subject for leveraging data mining techniques. The main purpose of this paper is presenting a hybrid geospatial data Clustering mechanism in order to achieve a high performance hotspot analysis method. The method basically works on 2 or 3-dimensional geographic coordinates of different natural and unnatural phenomena. It uses the systematic cooperation of two popular Clustering algorithms: the AGlomerative NEStive, as a hierarchical Clustering method and k-means, as a Partitional Clustering method. It is claimed that the hybrid method will inherit the low time complexity of the κ-means algorithm and also relative independency from user’s knowledge of the AGNES algorithm.Thus, the proposed method is expected to be faster than AGNES algorithm and also more accurate thanκ-means algorithm. Finally, the method was evaluated against two popular Clustering measurement criteria. The first Clustering evaluation criterion is adapted from Fisher’s separability criterion, and the second one is the popular minimum total distance measure. Results of evaluation reveal that the proposed hybrid method results in an acceptable performance. It has a desirable time complexity and also enjoys a higher cluster quality than its parents (AGNES and k-means). Real-time processing of hotspots requires an efficient approach with low time complexity. So, the problem of time complexity has been taken into account in designing the proposed approach.

Purpose: Clustering and co-word analysis is a method to reveal relationships and links and illustrate the intellectual structure of a scientific field. This research tries to study the intellectual structure of articles in the field of futures studies in Iran by using the technique of co-word analysis. Method: The current research is a descriptive-analytical development with a scientometric approach. The statistical population is 921 articles retrieved records in the field of futures studies. Findings: The findings showed that articles in the field of futures studies in Iran are often associated with positive growth, and in terms of frequency, the keywords scenario, Islamic Republic, and foresight are the most frequent in futures studies. The findings related to the hierarchical Clustering led to the formation of 8 clusters in this field, namely "ICT visions", "geographers who love the future", "knowledge development", " Futuristic higher education", "Future of Religion", "Regional Relations", "Strategic Foresight" and "Heavy Weight of Method". Conclusion: According to the findings of the current research and the high frequency of the keyword scenario, as well as the density and relationships of this keyword with other keywords, it can be concluded that the scenario is the dominant approach in futures studies. Also, according to the resulting clusters, it was observed that these researches have a high variety, but addressing the future in many areas is still neglected.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of Clustering-based-topic-detection, which are embedding methods, distance metrics, and Clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important Clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and Clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other Clustering algorithms.

Due to inadequate flood data it is not always possible to fit a frequency analysis to at-site stations. Reliable results are not always guaranteed by a single Clustering algorithm, so a combination of methods may be used. In this research, we considered three Clustering algorithms: single linkge, complete linkage and Ward (as hierarchial Clustering methods), and K-mean (as Partitional Clustering analysis). Hybrid cluster analysis was tested for up-to-dated of floods data in 68 hydrometric stations in East and NE of Iran. Four cluster validity indices were used to find the optimum number of clusters. Based on the Cophenetic coefficient and average Silhouette width, single linkge, and complete linkage methods were performed well, yet they produced nonconsistent clusters (one large and numerous small clusters) which are not amenable for flood frequency analysis.It was shown that hybridization was efficient to form homogeneous regions, however, the usefulness was dependent to the number of classes. Heterogeneity measure of Hosking was negative, due to inter-correlation of floods in the clusters. The hybrid of Ward and K-mean was shown to be the best combination for the region under study. Four homogeneous regions were delineated.