Publications
1- Designing a learning package for image processing algorithm using Viual Basic.Net 2008, for my BSc final year project I developed a windows application for Image processing algorithms using Visual Basic.net 2008
2- Semantic Web Mining Using Fuzzy C-Means Algorithm, for my master disseration I researched about semantic web data analysis by developing an web application using ASP.net with Bootsrap CSS framwork.
3- Mohammed, Wria Mohammed Salih, and Mohamad Mehdi Saraee. "Mining Semantic Web Data Using K-means Clustering Algorithm. 2016
click here to download the paper.
4- Semantic web Mining using Fuzzy C-means algorithm, 2016.
Click here to download the paper
5- Mining XML data using K-means and Manhattan algorithms, 2016
Abstract— over the last two decades, XML has astonishing developed for describing semi-structured data and exchanging data over the web. Thus, applying data mining techniques to XML data has become necessary. K-means clustering is one of the most popular algorithms in the clustering of data mining. Recently, there have been some researches undertaken on the mining XML data. In this paper, applying k-means algorithm, which is one of the clustering algorithms, on XML data is proposed. K-means as an algorithm chooses centroids and then clustering the XML data into groups according to the centroids. The comparison distances between each element vary with every centroid and will make groups of elements together. The closest elements from each others will be in the same group. The distances are measured using the Manhattan algorithm. In this research a specific application has been build, the application allows the user to upload an XML file, choose the target field and select the number of clusters. As a result, the application shows the clusters and centroids used in all of the steps. Index Terms— ASP.net, Centroids, Cluster, Data Mining, K-means, Manhattan, XML.
6- Mining RDF (Linked Data) using Eclat Algorithm
Abstract
Basket market analysis is one of the most widely used groups of data mining and have been extensively utilized for analyzing data to extract interesting information from huge amount of data. Also, Studies over the past two decades have provided important information on semantic web as it is part of (World Wide Web Consortium) W3C. Both data mining and semantic web have several key features to mine semi-structured dataset and having an accurate result. The methodological approach taken in this study is combining both Éclat algorithm and RDF (Resource Description Framework) dataset based on the process of converting RDF into dataset and mining it. Firstly, RDF data is checked for validation, and then it needs to convert into traditional dataset. This process requests SPARQL as a query language. Thus, it needs to imply Éclat (Equivalence class Transformation) algorithm on traditional dataset. This experiment illustrates that semantic web and data mining have significant results in mining semi-structured dataset. This paper hands out how mixing RDF and Éclat algorithm is influent. For this technique different data source can be used, however, for this paper particularly products in a supermarket are going to use as a main dataset.
Journal Papers
PENDING PUBLICATIONS: