Annotated Bibliography for the Research Proposal: Data Mining and Machine Learning

Paper Type:  Annotated bibliography
Pages:  4
Wordcount:  983 Words
Date:  2022-03-31


The research proposal will focus on data mining, machine learning techniques and the application of both technologies in different sectors. Peer-reviewed sources will be used in this study. I obtained the references by searching on online libraries utilizing various keywords such as data mining, machine learning, and data mining and machine learning algorithms.

Battula, Bhanu Prakash, and R. Satya Prasad. "An overview of recent machine learning strategies in data mining." International Journal of Advanced Computer Science and Applications 4.3 (2013): 50-54.

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In this article, Battula and Prasad focus on recent machine learning strategies. They begin by providing a brief introduction in data mining and some of the techniques utilized such as classification and tree diagrams. The article is then divided into various subheadings, which tackle the basics of data mining, measures used for modern learning techniques, and several research studies related to the different learning strategies. Some of the recent machine learning techniques mentioned by the authors include vocabulary-learning strategy, active learning technique, and source domain sample among others. The authors conclude that new machine learning techniques are favorable than the traditional ones because they mimic human learning.

The article is well researched and contains concrete information, which I think will be useful for my research proposal. I am planning to incorporate some significant ideas of this article in my research study.

Ali, Anwaar, Qadir, Junaid, Rasool, Raihan, Sathiase elan, Arjuna, and Awitter, Andrej. "Big data for development: applications and techniques." Big Data Analytics 1.1 (2016): 1-13.

Ali and others focus on educating the reader on some of the modern techniques used in machine learning, data mining, crowdsourcing and big data, and the internet of things. The authors briefly describe some of the methods utilized in machine learning, and they include supervised learning, which entails various techniques such as Naive Bayes classifiers, decision trees, and support vector machines. The second type of machine learning is unsupervised learning followed by reinforcement learning, deep learning, association rule learning, and numeric prediction. Some of the strategies mentioned in data mining include predictive analytics and NoSQL. The article contains relevant information, which will play a significant part in my literature review. I will utilize this article for my research proposal.

Gheware, S.D., Kejkar, A.S., and Tondare, S. M. "Data Mining: Task, Tools, Techniques and Applications." International Journal of Advanced Research in Computer and Communication Engineering 3. 10 (2014). 8095- 8098.

In this article, the authors focus on data mining techniques and its other related tasks. They argue that data mining is relevant since it serves as an alternative to traditional methods that are deemed unsuitable due to the high dimensionality of data and the enormity of data. In the article, knowledge discovery in databases is used simultaneously with data mining since data mining is a crucial component of the KDD process. Some of the data mining techniques mentioned in the article include statistical approach through regression analysis, cluster analysis, and Bayesian network. Machine learning techniques mentioned in the article include decision tree induction, inductive concept learning, and conceptual learning. The authors also focus on data mining tools, applications, and challenges. The article focuses on various aspects of data mining and machine learning making it a relevant source to refer to when formulating my research proposal.

Qiu, Junfei, Wu, Qihui, Ding, Guoru, Xu, Yuhua, and Feng, Shuo. "A survey of machine learning for big data processing." EURASIP Journal on Advances in Signal Processing 2016.1 (2016): 67.

In this issue, the authors focus on machine learning techniques, definition and classification of machine learning, advanced learning methods, latest research progress in machine learning, and other relevant topics. The authors argue that machine learning is a subject arising from various kinds of disciplines such as statistics and artificial intelligence. Recent learning methods are introduced in the article, and they include representation learning, deep learning, distributed and parallel learning, kernel-based learning, transfer learning, and active learning. This article contains significant information to assist me to write the research study using relevant ideas.

Silwattananusarn, Tipawan, and Kulthida Tuamsuk. "Data mining and its applications for knowledge management: a literature review from 2007 to 2012." International Journal of Data Mining & Knowledge Management Process (IJDKP) 2.5 (2012): 13-24.

Silwattananusarn and Kulthida focus on the application of data mining in knowledge management process. The authors evaluate the use of data mining techniques through a literature review of articles published from 2007 to 2012. Data mining was found significant for efficient capturing, storing, retrieving, and transferring of data. Under knowledge resources, the authors found out that data mining can be used to assist healthcare organizations, financial institutions, small and middle businesses, and entrepreneurial science. In knowledge management, data mining was found to have the following uses clustering, classification, and dependency modeling. The article provides a broad overview of the application of data mining in knowledge management. For this case, I will incorporate and use the ideas in this article.

Buczak, Anna L., and Erhan Guven. "A survey of data mining and machine learning methods for cybersecurity intrusion detection." IEEE Communications Surveys & Tutorials 18.2 (2016): 1153-1176.

The article focuses on how data mining and machine learning can be used for cybersecurity intrusion detection. The help of machine learning and data mining algorithms can be used to capture packet-level data through a programming interface called pcap. Some of the different techniques of machine learning and data mining applied for cybersecurity include artificial neural networks, anomaly detection, fuzzy association rule, and Bayesian network. The article is a significant piece of information for my research proposal.

Kumar, Dharminder, and Deepak Bhardwaj. "Rise of data mining: current and future application areas." IJCSI International Journal of Computer Science Issues 8.5 (2011).

In this article, the authors describe the current trends and applications of data mining. Data mining is identified to have various applications in business trends, bio-informatics, and cure for diseases, fight against terrorism, artificial intelligence, and machine learning, detecting eco-system disturbances, and statistics. This source will be relevant for my study because it captures various applications of data mining.

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