Search Pointers organize the main part of the application on the Internet. However, because of Information management hardware, high volume of data and word similarities in different fields the most answers to the users’ questions aren`t correct. So the web graph clustering and cluster placement in corresponding answers helps user to achieve his or her intended results. Community (web communities) can be used to generate automated directory services. In this paper the act of clustering has been done by finding the complete bipartite sub- graphs. The sub- graphs form the core of a community or clustering and by extending the core we can attain to the whole clustering. The whole set of graphs in England are 18 million pages and 300 million links.

Researchers have always been interested in graph nodes clustering based on content or structure. But less attention has been paid to clustering based on both structure and content. But a content-structural clustering is needed in information networks like social networks. In this paper, the ICS-Cluster algorithm is proposed which takes into consideration both the structure and content aspects of the nodes. The purpose of this approach is to gain a coherent internal structure (structural aspect) and homogeneous attribute values (content aspect) in the graph. In this approach firstly the graph is converted into a content-structural graph which edges' weight show similarity between the connected nodes. Incremental clustering is done based on edges’ weight in this process the edges with the most weight is considered as clusters then the weight of connected edge to the cluster is aggregated and they’ ll be one edge, the process is repeated until the algorithm reaches the number of clusters that indicated by the user. ICS-Cluster algorithm number of cluster is indicated by the user. Comparing ICS-Cluster with other content structural algorithm based on six criteria for measuring cluster quality shows that ICS-Cluster has good performance. These criteria contain structural criteria (Modularity, Error Link, and Density), content criterion (Average Similarity), content-structural criterion (CS-Measure) and the run time.

Entities in social networks, in addition to having the relationship with each other, also have content. This type of networks can be modeled by the enriched graph, in which nodes could have text too. graph clustering is one of the important attempts toward analyzing social networks. Despite these two facts, most of the existing graph clustering methods independently focused on one of the content or structural aspects. Content-Structural graph clustering algorithms simultaneously consider both the structure and the content of the graph. The main aim of this paper is to achieve well connected (structured) clusters while their nodes benefit from homogeneous attribute values (content). The proposed algorithm in this paper so-called RSL-Cluster performs the clustering by hierarchically removing the edge between nodes which has a weight lower that the average similarity of nodes. This stage continues until reaching the user’ s desired number of clusters. Comparing the proposed algorithm with three well-known content-structural clustering algorithms represents the proper functioning of the proposed method. The used measures to evaluate our method include structural, content and the content-structural measures.

The increase of cameras nowadays, and the power of the media in people's lives lead to a staggering amount of video data. It is certain that a method to process this large volume of videos quickly and optimally becomes especially important. With the help of video summarization, this task is achieved and the film is summarized into a series of short but meaningful frames or clips. This study tried to cluster the data by an algorithm (K-Medoids) and then with the help of a convolutional graph attention network, temporal and graph separation is done, then in the next step with the connection rejection method, noises and duplicates are removed, and finally summarization is done by merging the results obtained from two different graphical and temporal steps. The results were analyzed qualitatively and quantitatively on three datasets SumMe, TVSum, and OpenCv. In the qualitative method, an average of 88% accuracy rate in summarization and 31% error rate was achieved, which is one of the highest accuracy rates compared to other methods. In quantitative evaluation, the proposed method has a higher efficiency than the existing methods.

Density-based clustering algorithms are commonly used in machine learning and data mining due to their ability to identify clusters with different shapes and noisy objects. These algorithms are famous in data analysis and the use of their analysis output in industry and business. However, traditional clustering algorithms may have difficulty with datasets with different densities and overlapping neighboring clusters. To address these challenges, a new density-based clustering algorithm is proposed in this article. In this algorithm, the dependency matrix and the first-level search graph are used to find the dense points and the connection between the points, the concept of the relevant space is introduced to define the local and global density, and a central point identification method is used to identify the cluster structures. This algorithm also uses an allocation strategy based on the relevant space for the remaining objects to achieve accurate clustering results. Experimental results on real data sets show the effectiveness of the proposed method in clustering performance.

Today, with the spread of social networks, the opposition's efforts to chill out people from government (known as “ soft war” ) are increased. Therefore, dealing with this type of networks is important for military and security organizations. graph clustering is one of the first attempts toward analyzing social networks which can appropriately be modeled by a content graph. In contrast, most of the existing graph clustering methods independently focused on one of the content or structural aspects of a graph. The aim of this paper (implemented as CS-Cluster algorithm) is to achieve well connected clusters while their nodes benefits from homogeneous attribute values (content). In the second step of our research, after an intensive search, no measure has found which could simultaneously consider content and structural features of clustering algorithms. So to be able to appropriately evaluate our algorithm, a new content-structural measure (so-called “ CS-Measure” ) is proposed. Our experimentation shows that the proposed clustering algorithm outperforms two other well-known content-structural clustering algorithms, using the new content-structural, average similarity, and Error link measure as well as the previous content and structural measures, And it also performed relatively well in density measure.

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.