The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we construct an unsupervised learning schema for the k-means algorithm so that it is free of initializations without parameter selection and can also simultaneously find an optimal number of clusters. That is, we propose a novel unsupervised k-means (U-k-means) clustering algorithm with automatically finding an optimal number of clusters without giving any initialization and parameter selection. The computational complexity of the proposed U-k-means clustering algorithm is also analyzed. Comparisons between the proposed U-k-means and other existing methods are made. Experimental results and comparisons actually demonstrate these good aspects of the proposed U-k-means clustering algorithm.

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Unsupervised K-Means Clustering Algorithm

The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions.

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The k-means Algorithm: A Comprehensive Survey and Performance Evaluation

Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.

Educational data mining applications and tasks: A survey of the last 10 years

Withthe technological advent, the clustering phenomenon is recently being used in various domains and in natural language recognition. This article contributes to the clustering phenomenon of natural language and fulfills the requirements for the dynamic update of the knowledge system. This article proposes a method of dynamic knowledge extraction based on sentence clustering recognition using a neural network-based framework. The conversion process from natural language papers to object-oriented knowledge system is studied considering the related problems of sentence vectorization. This article studies the attributes of sentence vectorization using various basic definitions, judgment theorem, and postprocessing elements. The sentence clustering recognition method of the network uses the concept of prereliability as a measure of the credibility of sentence recognition results. An ART2 neural network simulation program is written using MATLAB, and the effect of the neural network on sentence recognition is utilized for the corresponding analysis. A postreliability evaluation indexing is done for the credibility of the model construction, and the implementation steps for the conjunctive rule sentence pattern are specifically introduced. A new method of structural modeling is utilized to generate the structured derivation relationship, thus completing the natural language knowledge extraction process of the object-oriented knowledge system. An application example with mechanical CAD is used in this work to demonstrate the specific implementation of the example, which confirms the effectiveness of the proposed method.

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Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

As COVID-19 spread worldwide, many major grain-producing countries have adopted measures to restrict their grain exports; food security has aroused great concern from various parties. How to improve grain production has become one of the most important issues facing all countries. However, crop diseases are a difficult problem for many farmers so it is important to master the severity of crop diseases timely and accurately to help staff take further intervention measures to minimize plants being further infected. In this paper, a restructured residual dense network was proposed for tomato leaf disease identification; this hybrid deep learning model combines the advantages of deep residual networks and dense networks, which can reduce the number of training process parameters to improve calculation accuracy as well as enhance the flow of information and gradients. The original RDN model was first used in image super resolution, so we need to restructure the network architecture for classification tasks through adjusted input image features and hyper parameters. Experimental results show that this model can achieve a top-1 average identification accuracy of 95% on the Tomato test dataset in AI Challenger 2018 datasets, which verifies its satisfactory performance. The restructured residual dense network model can obtain significant improvements over most of the state-of-the-art models in crop leaf identification, as well as requiring less computation to achieve high performance.

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Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

Clustering Arabic text documents is of high importance for many natural language technologies. This paper uses a combined method to cluster Arabic text documents. Mainly, we use generative models and clustering techniques. The study uses latent Dirichlet allocation and $k$ -means clustering algorithm and applies them to a news data set used in previous similar studies. The aim of this paper is twofold: it first shows that normalizing the weights in the vector space, for the document-term matrix of the text documents, dramatically improves the quality of clusters and hence the accuracy of clustering when using $k$ -means algorithm. The results are compared to a recent study on clustering Arabic text documents. Second, it shows that the combined method is superior in terms of clustering quality for Arabic text documents according to external measures, such as purity, F-measure, entropy, accuracy, and other measures. It is shown in this paper that the purity of the combined method is 0.933 compared to 0.82 for ${k}$ -means algorithm, and these figures are higher in comparison to a recent similar study. This is also confirmed by the other used validation measures. The correctness of the combined method is then confirmed using different Arabic data sets.

Revisiting K-Means and Topic Modeling, a Comparison Study to Cluster Arabic Documents

The Holy Quran is the reference book for more than 1.6 billion of Muslims all around the world Extracting information and knowledge from the Holy Quran is of high benefit for both specialized people in Islamic studies as well as non-specialized people. This paper initiates a series of research studies that aim to serve the Holy Quran and provide helpful and accurate information and knowledge to the all human beings. Also, the planned research studies aim to lay out a framework that will be used by researchers in the field of Arabic natural language processing by providing a ”Golden Dataset” along with useful techniques and information that will advance this field further. The aim of this paper is to find an approach for analyzing Arabic text and then providing statistical information which might be helpful for the people in this research area. In this paper the holly Quran text is preprocessed and then different text mining operations are applied to it to reveal simple facts about the terms of the holy Quran. The results show a variety of characteristics of the Holy Quran such as its most important words, its wordcloud and chapters with high term frequencies. All these results are based on term frequencies that are calculated using both Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) methods.

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Processing the text of the Holy Quran: a text mining study

Preprocessing Arabic text on social media.

Educational data mining (EDM) is a broader term that focuses on analyzing, exploring, predicting, clustering, and classification of data in educational institutions. EDM grows faster and covers many interdisciplinary such as education, e- learning, data mining, data analysis, intelligent system etc... The paper presents most relevant work in the area of EDM in higher education it covers course management systems, student behaviors, decision support system, and student retention and attrition. The paper also provide a comparison study between some of research work in such areas. Because of the growth in the interdisciplinary nature of EDM the paper, also try to provide boundary scope and definitions for EDM. Keywords Data Mining , DM, Educational Data Mining, EDM, Knowledge Discovery, KDD, Decision Support System, DSS, Course Management Systems, CMS. I. EDUCATIONAL DATA MINING DEFININTION

http://ijcttjournal.org/2016/Volume31/number-1/IJCTT-V31P109.pdf

The State of the Art on Educational Data Mining in Higher Education

This paper presents an approach for integrating data mining algorithms within Saudi university’s database system, viz., Prince Sattam Bin Abdulaziz University (PSAU) as a case study. The approach based on a bottom-up methodology; it starts by providing a data mining application that represents a solution to one of the problems that face Saudi Universities’ systems. After that, it integrates and implements the solution inside the university’s database system. This process is then repeated to enhance the university system by providing data mining tools that help different parties -especially decision makers- to carry out certain decision. The paper presents a case study that includes analyzing and predicting the student withdrawal from courses at PSAU using association rule mining, neural networks and decision trees. Then it provides a conceptual and practical approach for integrating the resulted application within the university’s database system. The experiment improves that this approach can be used as a framework for integrating data mining techniques within Saudi university’s database systems. The paper concluded that mining universities’ data can be applied as a computer system (intelligent university’s system), Also, data mining algorithms can be adapted with any database system regardless that this system is new, exists or legacy. Moreover, data mining algorithms can be a solution for some educational problems, in addition to providing information for decision makers and users

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Mohamed Osman Hegazi

Papers

Revisiting K-Means and Topic Modeling, a Comparison Study to Cluster Arabic Documents

Processing the text of the Holy Quran: a text mining study

Preprocessing Arabic text on social media.

The State of the Art on Educational Data Mining in Higher Education

An Approach for Integrating Data Mining with Saudi Universities Database Systems: Case Study