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.

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.

Identifying the needs and expectations of different customers in different market segments is the first step in implementing a targeted marketing strategy for each company. The market segmentation can exactly analyze the group of potential customers, whose needs are not well provided by current products, allows them to identify opportunities to produce new products. The purpose of this research is segmenting customer of nuts and dried fruits market. The statistical population of this research is the buyers of nuts and dried fruits in Mashhad County. After interviewing with the five experts of nuts & dried fruits, identified 32 expected values, then questionnaire was developed based on demographic variables and customers’ expected values and was distributed among the supplier stores of nuts and dried fruits using accidental non-probability sampling method in Mashhad County. The validity of the questionnaires increased based on the experts’ opinion and its reliability calculated using SPSS software. The Cronbach’s alpha coefficients were 0.957 for the whole questionnaire, which indicated high reliability of research tool. DBSCAN Algorithm and Rapid Miner software were used for data analysis and customer segmentation. Based on the findings of this research, three market segments of nuts and dried fruits customers; which are called Luxury Customers, Sensitive Customers and Ordinary Customers, were identified on the basis of demographic characteristics and their customers’ expected values. Finally, practical and managerial suggestions were provided according to customers’ expected values and demographic characteristics of each market segment.

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.

Detecting and tracking of moving objects is an important task in analyzing videos. In this paper, we propose a new method for tracking several concurrent moving objects of fixed camera. In the proposed method, at each stage, the location of moving objects in front of camera view is obtained information between two current and previous frames. In each step, Sift’s edge points is obtained based on previous frame and to get the correspondence of these feature points by the use of KLT feature point correspondence Algorithm on the current frame. Then having correspondent feature points between two sequence frames, we would estimate the distance by eliminating partial or fixed moving feature points related to moving objects. The classification of labeled features as moving objects is done using DBSCAN clustering Algorithm into different clusters. By this method and on each moment, the situation of all existing moving objects in camera view which has got by one by one correspondence between these objects, is determined. The obtained results of the proposed method shows a high degree of accuracy and acceptable consuming time to track moving objects.

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.

In the current state of colonization of near Earth space by satellites, there is an increasing need to know exactly the real status of occupation of this space. Thus, orbital parameters for all objects travelling in this space must be known with a high degree of accuracy, and this knowledge must be periodically updated, because this situation is always changing. Atmospheric drag, solar wind, moon and planetary gravitational perturbations, Earth oblateness, etc. are all sources of interference that generate orbital perturbations beyond what the best orbital model can predict. The solution is to periodically observe all the satellites, particularly the debris (because active satellites themselves contribute to maintain the knowledge of their orbital parameters), determine with precision their positions and update their known orbital parameters. There is a real need for sky surveillance in order to monitor either the satellites or the non-functional space objects for different purposes, such as to correct the satellites deviations from their trajectories, to detect uncataloged space debris objects and to avoid possible collisions. In order to define the location of the satellite in the sky and then to update its orbital parameters, an optical satellite tracking system can be designed which acquires sequences astronomical images from the sky. Such system is composed of many sensors like a telescope, a CCD camera, a GPS receiver, etc. Also, some reference data such as the star catalogues and the Two Lines Element (TLE) database are used. The telescope is used to search the sky and point to the satellite, precisely. The CCD camera acquires some sequences images in a current time provided by GPS. The star catalogues are employed to calibrate the image plane to the celestial coordinate systems. The TLE database contains the out-dated orbital parameters to estimate the satellite position. For this purpose an Algorithms and software that can automatically detect and report the presence of satellite streaks in the acquired images are needed. The Algorithms presented in this document were developed for this purpose. The image processing technique presented in this document is a collection of Algorithms used to detect and classify everything that can be observed in the image, such as stars, satellite streaks and image artefacts. First due to the use of digital imagery, the quality of digital images is critical and affects the final product. Different noises in imaging phase could degrade the quality of image, for this purpose the non-linear diffusion filter has been used. This technique, is based on the use of partial differential equations, the idea behind the use of the diffusion equation in image processing arose from the use of the Gaussian filter in multi-scale image analysis. Second for the removal of the image background the stars have been detected using SIFT method. In this method the star's centers are extracted with sub-pixel precision, then they have been subtracted from image in an iteration producer. Third the clustering method has been applied for satellite streak detection. In this way the Density-based spatial clustering of applications with noise (DBSCAN) which is a density-based clustering Algorithm has been used, finally MSAC Algorithm has been implemented for streak model extraction.

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.