Biblio

Thanks to the rapid increase in technology and electronic communications, e-mail has become a serious communication tool. In many applications such as business correspondence, reminders, academic notices, web page memberships, e-mail is used as primary way of communication. If we ignore spam e-mails, there remain hundreds of e-mails received every day. In order to determine the importance of received e-mails, the subject or content of each e-mail must be checked. In this study we proposed an unsupervised system to classify received e-mails. Received e-mails' coordinates are determined by a method of natural language processing called as Word2Vec algorithm. According to the similarities, processed data are grouped by k-means algorithm with an unsupervised training model. In this study, 10517 e-mails were used in training. The success of the system is tested on a test group of 200 e-mails. In the test phase M3 model (window size 3, min. Word frequency 10, Gram skip) consolidated the highest success (91%). Obtained results are evaluated in section VI.

In the last several decades digital images were extend their usage in numerous areas. Due to various digital image processing methods they became part areas such as astronomy, agriculture and more. One of the main task in image processing application is segmentation. Since segmentation represents rather important problem, various methods were proposed in the past. One of the methods is to use clustering algorithms which is explored in this paper. We propose k-means algorithm for digital image segmentation. K-means algorithm's well known drawback is the high possibility of getting trapped into local optima. In this paper we proposed brain storm optimization algorithm for optimizing k-means algorithm used for digital image segmentation. Our proposed algorithm is tested on several benchmark images and the results are compared with other stat-of-the-art algorithms. The proposed method outperformed the existing methods.

How to protect users' private data during network data mining has become a hot issue in the fields of big data and network information security. Most current researches on differential privacy k-means clustering algorithms focus on optimizing the selection of initial centroids. However, the traditional privacy budget allocation has the problem that the random noise becomes too large as the number of iterations increases, which will reduce the performance of data clustering. To solve the problem, we improved the way of privacy budget allocation in differentially private clustering algorithm DPk-means, and proposed APDPk-means, a new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation. APDPk-means decomposes the total privacy budget into a decreasing arithmetic progression, allocating the privacy budgets from large to small in the iterative process, so as to ensure the rapid convergence in early iteration. The experiment results show that compared with the other differentially private k-means algorithms, APDPk-means has better performance in availability and quality of the clustering result under the same level of privacy protection.