clustering

Using the Internet of Things (IoT) for intelligence of buildings and homes has posed many challenges. One of the main challenges in implementing IoT is the energy consumption of its equipment, so that leads to the short life time of equipment in networks like this. Various solutions have been proposed to achieve the goal of reducing energy consumption and increasing the life of nodes. The purpose of this study is to reduce energy consumption, increase the number of packets sent to the destination, increase the equipment life time in this type of network, and cover them more. To achieve this goal, the LEACH protocol is proposed instead of the probability distribution of fuzzy logic, in the cluster selection phase. The LEACH routing protocol is an organized one that distributes loads over sensor networks. In this protocol, nodes organize themselves into local groups, with one node in the group acting as the group leader. In this proposed method, a very small number of nodes are targeted in active mode for tracking, and the remaining nodes are switched to inactive (dormant) position. In this paper, the proposed method is compared with the H-LEACH protocol, which is the basis of tracking in important articles published exactly under the same conditions. Duo to boosting remaining energy in this method by 49.75% and reducing the sent packages by 36.44%, it is concluded that the proposed method performs better than the H-LEACH protocol in increasing the network life.

The purpose of this study was to identify and discover unknown models and relationships and to identify cases outside the models, from the huge volume of data in the educational system of the University of Applied Science & Technology. For this purpose, the scores of 19207 students/courses in 8 courses, 6 provinces and 120 educational centers that have been centrally held throughout the country in the second semester of the academic year 1397-98 have been studied. Statistical analyses and data mining were performed using SPSS Modeler software. First, statistics were calculated and the relationship between different features and courses was examined. Then, by K-means clustering, 5 clusters were obtained. Cluster 1 is for poor students, who have low test scores. Cluster 2 is for excellent students who have a high number of credits and a high-test score. Cluster 4 is for excellent students who have taken fewer credits. These students have even higher test scores than cluster 2 students. Cluster 5 corresponds to the small number of credits taken, which includes conditional students. Students in this cluster have better test scores than weak students (cluster 1). Then the relationship between different variables was investigated. Among the variables, there is a direct relationship between test score and grade point average. The results of the research can be effective in advancing the educational goals of the university.

Strategic planning in Iranian universities and institutes of higher education is generally prepared using strategic planning models introduced by experts and other universities. These programs will be published in the form of university strategic planning documents.
These documents have such features that can be similar or different than the programming templates used. Existence of the similar properties in documents makes it possible to group them. In this study, we try to use quantitative (Statistical) methods for grouping similar documents into homogeneous groups. For this purpose, the content and formal characteristics of the planning documents of eight different universities, including three governmental University (Tehran, Tarbiat Modares and Ferdowsi), three nongovernmental universities and institutes of higher education (science and culture, Soreh and Ashrafi Esfahani), and two foreign universities (Oxford and The Cornell) has been investigated and the presence or absence of these features has been determined and then Using clustering as a multivariate statistical method, these universities were grouped into 4 different clusters.
In order to determine the model which is used in compiling strategic planning documents of the selected universities, the characteristics of 12 different strategic planning models have been studied, and the similarity of each of them with the investigated documents has been determined.
The results of this study showed that investigated strategic planning documents were grouped into four groups, and each of these documents probably used a distinctive pattern than the other groups.
The result of this quantitative survey can be used as an introduction to the qualitative analysis of the documents under review.
In addition, the survey results showed that none of the selected Iranian universities designed their own specific model for strategic planning.
In other words, the models used in the planning of the universities under study were generally adaptive and the specific approach for none of the  universities was not considered. This matter can challenge the success of universities in the operationalizing of derived planning documents.

In the information era, customizing learning process is very important in most learning systems. E-learning as a learning system requires a customized mechanism for better learning process. Recently, educational data mining methods are widely used in the context of e-learning systems. In fact, using data mining techniques, researchers try to better recognize learners and create a customized learning program. In this paper, a new model to cluster learners based on their learning style has been proposed. In this model, for learners, using Felder-Solomon questionnaire different dimensions of Felder and Silverman learning style are measured. Subsequently, using k-means clustering algorithm, the learners are categorized in different groups. In order to evaluate the model, it has been used and evaluated in a real learning program. Evaluation results shows the effectiveness of the proposed model since the learners show better educational performance. Moreover, training process become more.