The fuzzy-C-means (FCM) ALGORITHM is one of the most famous fuzzy clus-tering ALGORITHMs, but it gets stuck in local optima. In addition, this algo-rithm requires the number of clusters. Also, the density-based spatial of the application with noise (DBSCAN) ALGORITHM, which is a density-based clus-tering ALGORITHM, unlike the FCM ALGORITHM, should not be pre-numbered. If the clusters are specific and depend on the number of clusters, then it can determine the number of clusters. Another advantage of the DBSCAN clus-tering ALGORITHM over FCM is its ability to cluster data of different shapes. In this paper, in order to overcome these limitations, a hybrid approach for CLUSTERING is proposed, which uses FCM and DBSCAN ALGORITHMs. In this method, the optimal number of clusters and the optimal location for the centers of the clusters are determined based on the changes that take place according to the data set in three phases by predicting the possibility of the problems stated in the FCM ALGORITHM. With this improvement, the values of none of the initial parameters of the FCM ALGORITHM are random, and in the first phase, it has been tried to replace these random values to the optimal in the FCM ALGORITHM, which has a significant effect on the convergence of the ALGORITHM because it helps to reduce iterations. The proposed method has been examined on the Iris flower and compared the results with basic FCM   ALGORITHM and another ALGORITHM. Results shows the better performance of the proposed method.

Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can solve the problem of the cold start of the system and improve the quality of the search. In this research, a new method is presented in order to improve recommender systems in the field of the web, which uses the DBSCAN CLUSTERING ALGORITHM to cluster data, and this ALGORITHM obtained an efficiency score of 99%. Then, using the Page rank ALGORITHM, the user's favorite pages are weighted. Then, using the SVM method, we categorize the data and give the user a combined recommender system to generate predictions, and finally, this recommender system will provide the user with a list of pages that may be of interest to the user. The evaluation of the results of the research indicated that the use of this proposed method can achieve a score of 95% in the recall section and a score of 99% in the accuracy section, which proves that this recommender system can reach more than 90%. It detects the user's intended pages correctly and solves the weaknesses of other previous systems to a large extent.

Feature selection is an important step in most classification problems to select an optimal subset of features to increase the learning accuracy and reduce the computational time. In this paper we proposed a new feature CLUSTERING based method to perform feature selection (FFS) in classification problems. The FFS ALGORITHM works in two steps. In the first step, features are divided into clusters by using F-DBSCAN method. A novel F-DBSCAN CLUSTERING method used mutual information for measuring dependencies between features. In the second step, the most representative feature is selected from each cluster by a new criterion function. This allows us to consider the possible dependency on the target class and the redundancy between the selected features in each cluster. The experimental results on different datasets show that the proposed ALGORITHM is more effective for feature selection in classification problems. Compared with the other methods, the average classification accuracy of C4.5, KNN and Naïve Bayes are improved using FFS by 8.05, 8.36 and 4.63 percent, respectively. Also, the results demonstrate that the FFS ALGORITHM produces small subsets of features with very high classification rate.

A recommendation system is a system that, based on a limited amount of information provided by users as well as the feedback given to goods, persons, and locations by other users, provides appropriate suggestions to the user. Today, with the large number of physicians and specialists, it seems necessary to have a system for identifying the right specialist and experienced physician for the patient. We present in this study a system for medical recommendations that analyzes physicians and specialists. It uses collaborative filtering and scores provided by other users to suggest physician recommendations according to the area of expertise of the physician. Research conducted and evaluation of results show that this system can successfully recommend a specialist doctor to the user in 90% of cases.

CLUSTERING is one of the important techniques for knowledge discovery in spatial databases. density-based CLUSTERING ALGORITHMs are one of the main CLUSTERING methods in data mining. DBSCAN which is the base of density-based CLUSTERING ALGORITHMs, besides its benefits suffers from some issues such as difficulty in determining appropriate values for input parameters and inability to detect clusters with different densities.In this paper, we introduce a new CLUSTERING ALGORITHM which unlike DBSCAN ALGORITHM, can detect clusters with different densities. This ALGORITHM also detects nested clusters and clusters sticking together. The idea of the proposed ALGORITHM is as follows. First, we detect the different densities of the dataset by using a technique and Eps parameter is computed for each density. Then DBSCAN ALGORITHM is adapted with the computed parameters to apply on the dataset. The experimental results which are obtained by running the suggested ALGORITHM on standard and synthetic datasets by using well-known CLUSTERING assessment criteria are compared to the results of DBSCAN ALGORITHM and some of its variants including VDBSCAN, VMDBSCAN, LDBSCAN, DVBSCAN and MDDBSCAN. All these ALGORITHMs have been introduced to solve the problem of multi-density data sets. The results show that the suggested ALGORITHM has higher accuracy and lower error rate in comparison to the other ALGORITHMs.

CLUSTERING is one of the main tasks in data mining, which means grouping similar samples. In general, there is a wide variety of CLUSTERING ALGORITHMs. One of these categories is density-based CLUSTERING. Various ALGORITHMs have been proposed for this method; one of the most widely used ALGORITHMs called DBSCAN. DBSCAN can identify clusters of different shapes in the dataset and automatically identify the number of clusters. There are advantages and disadvantages in this ALGORITHM. It is difficult to determine the input parameters of this ALGORITHM by the user. Also, this ALGORITHM is unable to detect clusters with different densities in the data set. ISB-DBSCAN ALGORITHM is another example of density-based ALGORITHMs that eliminates the disadvantages of the DBSCAN ALGORITHM. ISB-DBSCAN ALGORITHM reduces the input parameters of DBSCAN ALGORITHM and uses an input parameter k as the nearest neighbor's number. This method is also able to identify different density clusters, but according to the definition of the new core point, It is not able to identify some clusters in a different data set. This paper presents a method for improving ISB-DBSCAN ALGORITHM. A proposed approach, such as ISB-DBSCAN, uses an input parameter k as the number of nearest neighbors and provides a new definition for core point. This method performs CLUSTERING in three steps, with the difference that, unlike ISB-DBSCAN ALGORITHM, it can create a new cluster in the final stage. In the proposed method, a new criterion, such as the number of dataset dimensions used to detect noise in the used data set. Since the determination of the k parameter in the proposed method may be difficult for the user, a new method with genetic ALGORITHM is also proposed for the automatic estimation of the k parameter. To evaluate the proposed methods, tests were carried out on 11 standard data sets and the accuracy of CLUSTERING in the methods was evaluated. The results showe that the proposed method is able to achieve better results in different data sets compare to other available methods. In the proposed method, the automatic determination of k parameter also obtained acceptable results.

The detection and prevention of crime, in the past few decades, required several years of research and analysis. However, today, thanks to smart systems based on data mining techniques, it is possible to detect and prevent crime in a considerably less time. Classification and CLUSTERING-based smart techniques can classify and cluster the crime-related samples. The most important factor in the CLUSTERING technique is to find the centrality of the clusters and the distance between the samples of each cluster and the center of the cluster. The problem with CLUSTERING techniques, such as k-modes, is the failure to precisely detect the centrality of clusters. Therefore, in this paper, Elephant Herding Optimization (EHO) ALGORITHM and k-modes are used for CLUSTERING and detecting the crime by means of detecting the similarity of crime with each other. The proposed model consists of two basic steps: First, the cluster centrality should be detected for optimized CLUSTERING; in this regard, the EHO ALGORITHM is used. Second, k-modes are used to find the clusters of crimes with close similarity criteria based on distance. The proposed model was evaluated on the Community and Crime dataset consisting of 1994 samples with 128 characteristics. The results showed that purity accuracy of the proposed model is equal to 91. 45% for 400 replicates.

With regard to the non-linear nature of real-life data, their clusters' shapes are non-convex and unfortunately, some CLUSTERING methods cannot identify non-convex clusters and this is a challenge. Density-based CLUSTERING methods could be a solution to this problem. Among all methods of this type, the DBSCAN ALGORITHM can cluster data with different shapes, sizes, and densities and also identify noise points. However, owing to the use of static input parameters-the neighbourhood radius ($Eps$) and the minimum value for cluster formation ($MinPts$)- this ALGORITHM has some problems such as the difficulty in accurately determining these parameters in high-dimensional data sets and not recognizing clusters with different densities. Accordingly, this paper presents a density CLUSTERING ALGORITHM, which requires minimal input parameters and one of its main parameters is $Eps$, which is automatically calculated based on the $k$-nearest neighbours of points and its value is different for each cluster. To evaluate the effectiveness of the proposed ALGORITHM, some experiments were conducted. The obtained results showed the effectiveness and efficiency of the presented ALGORITHM regarding the correct identification of clusters with the desired shape, size, and density. In addition, the proposed ALGORITHM was found effective in estimating the number of clusters in most of the data sets considered in this study.