جستجوی مقالات مرتبط با کلیدواژه « diversity » در نشریات گروه « برق »

Data clustering is one of the main steps in data mining, which is responsible for exploring hidden patterns in non-tagged data. Due to the complexity of the problem and the weakness of the basic clustering methods, most studies today are guided by clustering ensemble methods. Diversity in primary results is one of the most important factors that can affect the quality of the final results. Also, the quality of the initial results is another factor that affects the quality of the results of the ensemble. Both factors have been considered in recent research on ensemble clustering. Here, a new framework for improving the efficiency of clustering has been proposed, which is based on the use of a subset of primary clusters, and the proposed method answers the above questions and ambiguities. The selection of this subset plays a vital role in the efficiency of the assembly. Since evolutionary intelligent algorithms have been able to solve the majority of complex engineering problems, this paper also uses these intelligent methods to select subsets of primary clusters. This selection is done using three intelligent methods (genetic algorithm, simulation annealing and particle swarm optimization). In this paper a clustering ensemble method is proposed which is based on a subset of primary clusters. The main idea behind this method is using more stable clusters in the ensemble. The stability is applied as a goodness measure of the clusters. The clusters which satisfy a threshold of this measure are selected to participate in the ensemble. For combining the chosen clusters, a co-association based consensus function is applied. A new EAC based method which is called Extended Evidence Accumulation Clustering, EEAC, is proposed for constructing the Co-association Matrix from the subset of clusters. Experimental results on several standard datasets with normalized mutual information evaluation, Fisher and accuracy criteria compared to Alizadeh, Azimi, Berikov, CLWGC, RCESCC, KME, CFSFDP, DBSCAB, NSC and Chen methods show the significant improvement of the proposed method in comparison with other ones.

One of the wireless networks that have been studied in recent years is the WiMAX network. Multi-antenna techniques are used in WiMAX systems to increase coverage and reliability of transmission and reception. The design of multiple antennas provides many benefits such as array gain, diversification gain, and elimination of co-channel interference. Multi-antenna technology support is a key feature of WiMAX compared to other wireless broadband technologies (such as HSDPA). To increase coverage and reliability, IEEE 802.16-2004 standard WiMAX systems support multiple antenna techniques such as space-time coding (STC), array antenna systems (AAS), and MIMO systems. In this paper, we focus on techniques that do not require channel information in the transmitter, such as STC and MIMO and compare the results.

One of the main challenges of increasing information in the new era, is to find information of interest in the mass of data. This important matter has been considered in the design of many sites that interact with users. Recommender systems have been considered to resolve this issue and have tried to help users to achieve their desired information; however, they face limitations. One of the most important challenges that they face is cold-start problem, which is raised when a new user/item entered into the system, while no previous information is available for it. The lack of previous knowledge of the new user/item, will causes the system fails generating its suggestions normally. In this paper, to solve the problem of cold-start user/item a new method is presented using combining content-based models and collaborative filtering. Moreover, demographic data is used to recommend the nearest items to cold-start users/items' interests. Compared to existing methods, the evaluation results show that the proposed method reduces the MAE and RMSE error.