جستجوی مقالات مرتبط با کلیدواژه « k-means algorithm » در نشریات گروه « ریاضی »

With the advancements in science and technology‎, ‎the industrial and aviation sectors have witnessed a significant increase in data‎. ‎A vast amount of data is generated and utilized continuously‎. ‎It is imperative to employ data mining techniques to extract and uncover knowledge from this data‎. ‎Data mining is a method that enables the extraction of valuable information and hidden relationships from datasets‎. ‎However‎, ‎the current aviation data presents challenges in effectively extracting knowledge due to its large volume and diverse structures‎. ‎Air Traffic Management (ATM) involves handling Big data‎, ‎which exceeds the capacity of conventional acquisition‎, ‎matching‎, ‎management‎, ‎and processing within a reasonable timeframe‎. ‎Aviation Big data exists in batch forms and streaming formats‎, ‎necessitating the utilization of parallel hardware and software‎, ‎as well as stream processing‎, ‎to extract meaningful insights‎. ‎Currently‎, ‎the map-reduce method is the prevailing model for processing Big data in the aviation industry‎. ‎This paper aims to analyze the evolving trends in aviation Big data processing methods‎, ‎followed by a comprehensive investigation and discussion of data analysis techniques‎. ‎We implement the map-reduce optimization of the K-Means algorithm in the Hadoop and Spark environments‎. ‎The K-Means map-reduce is a crucial and widely applied clustering method‎. ‎Finally‎, ‎we conduct a case study to analyze and compare aviation Big data related to air traffic management in the USA using the K-Means map-reduce approach in the Hadoop and Spark environments‎. ‎The analyzed dataset includes flight records‎. ‎The results demonstrate the suitability of this platform for aviation Big data‎, ‎considering the characteristics of the aviation dataset‎. ‎Furthermore‎, ‎this study presents the first application of the designed program for air traffic management‎.

One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock exchange market. This research proposes a smart algorithm by means of valuable big data that is generated by stock exchange market and different kinds of methodology to present a smart model.In this paper, we investigate relationships between the data and access to their latent information with an enormous amount of data which has a significant impact on the investor’s decisions. First, extracting technical indicators from different point of the charts based on two groups of stock exchanges like petrochemical and automotive during 1387 to 1396, then analyzing clusters by means of k-means algorithm and data mining methodology. The contributions of this paper are: 1. To create a model with twenty technical indicators in different stock exchange companies and industries.2. To evaluate the proposed model and finally to predict the sales signals at the maximum points which has significant performance and can be predicted with acceptable accuracy.

This paper presents a method for performance evaluation, ranking and clustering based on the double-frontier view to analyze the complex networks. The model allows us to open the structure of the “black box” and can help to obtain important information about efficient and inefficient points of the system. In this paper, we consider a three-stage network, in respect to the additional desirable and undesirable inputs and outputs and utilize the cooperative approach to measure the efficiency of the overall system. Due to the fact that, a conclusion implying only one of these two, optimistic or pessimistic views is one-sided and incomplete, so, in this paper we used the double-frontier to analyze the network. Moreover, a heuristic technique was used to convert non-linear models into linear models. After obtaining the effective and inefficient points of the network, the DMUs are classified into several clusters by the k-means algorithm.Finally, in this article, in order to apply the proposed model a factory producing dairy products with a production area, warehouse premises and a delivery point are simulated. This factory has been regarded as a dynamic network with a time period of 24 intervals. The results of the ranking showed that, the time periods, (10) and (1) were the best and poorest respectively, in context to the efficiency within 24 phases of time.