One of the most important aspects of software project management is the estimation of cost and time required for running information system. Therefore, software managers try to carry estimation based on behavior, properties, and project restrictions. Software cost estimation refers to the process of development requirement prediction of software system. Various kinds of effort estimation patterns have been presented in recent years, which are focused on intelligent techniques. This study made use of CLUSTERING approach for estimating required effort in software projects. The effort estimation is carried out through SWR (Step Wise Regression) and MLR (Multiple Linear Regressions) regression models as well as CART (Classification And Regression Tree) method. The performance of these methods is experimentally evaluated using real software projects. Moreover, CLUSTERING of projects is applied to the estimation process. As indicated by the results of this study, the combination of CLUSTERING method and algorithmic estimation techniques can improve the accuracy of estimates.

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.

While Very High-Resolution (VHR) imagery is favored for change detection due to its spatial detail, it presents challenges, notably intricate feature interactions and noise, complicating precise change identification. Addressing this, this paper introduces an unsupervised method for detecting building changes in Very High-Resolution (VHR) images, integrating the strengths of Principal Component Analysis (PCA) and K-Means CLUSTERING with a focus on building changes. Initially, PCA is employed to reduce data dimensionality, emphasizing the most significant variations across temporal datasets. The difference between the PCA-transformed images is computed, revealing areas of potential change. K-means CLUSTERING then categorizes these regions based on their pixel values, labeling them as either changed or unchanged. A unique step in our approach is the building index extraction. This step refines the building detection by identifying contours in the segmented images based on their properties, such as area and perimeter emphasizing true building alterations and filtering out unrelated landscape changes. Experimental results on benchmark datasets, LEVIR-CD and CLCD, showcase the superior performance of the method, with an overall accuracy of 0. 97 and a Kappa coefficient of 0. 89. These results highlight the effectiveness of the proposed approach for building change detection in remote sensing and urban monitoring applications.

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.

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.

Identifying clusters or CLUSTERING is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K-Means, which alters the convergence method of K-Means algorithm to provide more accurate CLUSTERING results than the K-means algorithm and its variants by increasing the clusters’ coherence. Persistent K-Means uses an iterative approach to discover the best result for consecutive iterations of KMEANS algorithm.