Data Mining - Concepts and Techniques.

Ship detection on spaceborne images has attracted great interest in the applications of maritime security and traffic control. Optical images stand out from other remote sensing images in object detection due to their higher resolution and more visualized contents. However, most of the popular techniques for ship detection from optical spaceborne images have two shortcomings: 1) Compared with infrared and synthetic aperture radar images, their results are affected by weather conditions, like clouds and ocean waves, and 2) the higher resolution results in larger data volume, which makes processing more difficult. Most of the previous works mainly focus on solving the first problem by improving segmentation or classification with complicated algorithms. These methods face difficulty in efficiently balancing performance and complexity. In this paper, we propose a ship detection approach to solving the aforementioned two issues using wavelet coefficients extracted from JPEG2000 compressed domain combined with deep neural network (DNN) and extreme learning machine (ELM). Compressed domain is adopted for fast ship candidate extraction, DNN is exploited for high-level feature representation and classification, and ELM is used for efficient feature pooling and decision making. Extensive experiments demonstrate that, in comparison with the existing relevant state-of-the-art approaches, the proposed method requires less detection time and achieves higher detection accuracy.

Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine

Data clustering is an important data exploration technique with many applications in data mining. K- means is one of the most well known methods of data mining that partitions a dataset into groups of patterns, many methods have been proposed to improve the performance of the K-means algorithm. Standardization is the central preprocessing step in data mining, to standardize values of features or attributes from different dynamic range into a specific range. In this paper, we have analyzed the performances of the three standardization methods on conventional K-means algorithm. By comparing the results on infectious diseases datasets, it was found that the result obtained by the z-score standardization method is more effective and efficient than min-max and decimal scaling standardization methods.

Standardization and Its Effects on K-Means Clustering Algorithm

In this paper, we propose a comparison of human gesture recognition using data mining classification methods in video streaming. In particular, we are interested in a specific stream of vector of twenty body-joint positions which are representative of the human body captured by Kinect camera. The recognized gesture patterns of the study are stand, sit down, and lie down. Classification methods chosen for comparison study are backpropagation neural network, support vector machine, decision tree, and naive Bayes. Experimental results have shown that the backpropagation neural network method outperforms other classification methods and can achieve recognition with 100% accuracy. Moreover, the average accuracy of all classification methods used in this study is 93.72%, which confirms the high potential of using the Kinect camera in human body recognition applications. Our future work will use the knowledge obtained from these classifiers in time series analysis of gesture sequence for detecting fall motion in a smart home system.

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Human gesture recognition using Kinect camera

Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success, through which student attributes to focus on, up to which machine learning method is more appropriate to the given problem. This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.

https://link.springer.com/content/pdf/10.1186%2Fs41239-020-0177-7.pdf

Predicting academic success in higher education: literature review and best practices

This study is emphasized on different types of normalization. Each of which was tested against the ID3 methodology using the HSV data set. Number of leaf nodes, accuracy and tree growing time are three factors that were taken into account. Comparisons between different learning methods were accomplished as they were applied to each normalization method. A new matrix was designed to check for the best normalization method based on the factors and their priorities. Recommendations were concluded.

/pdf/data-mining-a-preprocessing-engine-4rm5x5353y.pdf

Data Mining: A Preprocessing Engine

Most information systems usually have some missing values due to unavailable data Missing values minimizing the quality of classification rules generated by a data mining system Missing vales also affecting the quantity of classification rules achieved by the data mining system Missing values could influence the coverage percentage and number of reducts generated Missing values lead to the difficulty of extracting useful information from that data set Solving the problem of missing data is of a high priority in the field of data mining and knowledge discovery Replacing missing values by a specific value should not affect the quality of the data Four different models for dealing with missing data were studied A framework is established that remove inconsistencies before and after filling the attributes of missing values with the new expected value as generated by one of the four models Comparative results were discussed and recommendations were concluded

A framework to Deal with Missing Data in Data Sets

Ontology plays an essential role in recognizing the meaning of the information in Web documents. It has been shown that extracting concepts is easier than building relationships among them. For a defined set of concepts, many existing algorithms produce all possible relationships for that set. This makes the process of refining the relationships almost impossible. A new algorithm is needed to reduce the number of relationships among a defined set of concepts produced by existing algorithms. This article contributes such an algorithm, which enables a domain-knowledge expert to refine the relationships linking a set of concepts. In the research reported here, text-mining tools have been used to extract concepts in the domain of e-commerce laws. A new algorithm has been proposed to reduce the number of extracted relationships. It groups the concepts according to the number of relationships with other concepts and provides formalization. An experiment and software have been built, proving that reducing the number of relationships will reduce the efforts needed from a human expert. © 2008 Wiley Periodicals, Inc.

Luai Al Shalabi

Papers

Data Mining: A Preprocessing Engine

A framework to Deal with Missing Data in Data Sets

Building ontological relationships: A new approach