Machine Learning in Data Mining for Knowledge Discovery

Dear Colleagues,

It is our pleasure to announce a new Special Issue of the journal Big Data and Cognitive Computing titled “Machine Learning in Data Mining for Knowledge Discovery”.

Data mining was introduced in the 1930s as a strategy for obtaining knowledge from data. Starting from uncleaned and unstructured data and ultimately yielding useful knowledge (e.g., patterns, rules, and any other entities), there are a few steps involved. While machine learning has been applied to each step of the data processing, due to reasons such as the explosion of the volume of data, the distribution of data, data sparsity, and partially missing or invalid data, generic data mining technologies may need to be adapted and new approaches are desired in the Big Data era. The challenge is that with most machine learning approaches (specifically non-symbolic approaches), although they have significant performance, the result might not be understandable by humans and thus might be difficult to apply in practice. On the other hand, symbolic methods are suitable in occasions where people are more interested in the form of knowledge that can be easily understood and thus further infer practical actions manually. Another area that has not been given enough attention is how to utilize the mined patterns and rules and evaluate the benefits and costs of these in practice. Furthermore, synthetic studies combining both symbolic and non-symbolic approaches might be a potential direction that improves both human understanding and usability.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

1. Association rules mining in Big Data;
2. Classification rules mining in Big Data;
3. Unstructured data cleansing;
4. Distributed data mining;
5. Adaptation of deep learning approaches to data mining;
6. Actionable rules mining in Big Data;
7. Neural networks for knowledge discovery;
8. Generic machine learning model studies in data mining;
9. Knowledge evaluation and quantization;
10. Utility analysis for rules and patterns;
11. Cost–benefit analysis for rules and patterns;
12. Redundant data removal and its quality evaluation;
13. Attribute reduction;
14. Social network discovery;
15. Data integration and data fusion from a variety of sources.

Dr. Cong Gao
Dr. Chuntao Ding
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data mining
• Big Data
• machine learning
• association rules
• actionable rules
• expected pattern
• knowledge discovery
• attribute reduction
• pattern evaluation