Text Mining and Data Mining

Dear Colleagues,

In today's world, it is a complex task to gather, analyze, and extract information from huge amount of datasets. So, we use many efficient methods for the practical integration of the data. Text mining is a technique based around applying knowledge discovery techniques to unstructured text and termed knowledge discovery in text, text data mining, or text mining. Data mining technology is giving us the ability to extract meaningful patterns from large quantities of structured data. Information retrieval systems have made large quantities of textual data available, as well as the scope of multimodal data, in particular multimodal information extraction, which focuses knowledge discovery on multimodal data from various modalities such as image, text, and video, as well as image-aided information extraction, which enhances the performance of information extraction with image information, image information extraction, which extracts structured data from images, etc.

Text mining and data mining use diverse techniques such as natural language processing, computer vision, machine learning, information retrieval, and knowledge management for the automated analysis of digital content. By doing so, text mining and data mining can extract information, identify patterns, and discover new trends, insights, and correlations.

This Special Issue seeks original, unpublished articles that address recent advances in data mining and text mining techniques as well as their applications. Topics of interest include, but are not limited to, the following: text mining; knowledge discovery in text; content analysis; text analysis; text classification; audio-to-text mining; video-to-text mining; image-to-text mining; big data; data mining; artificial intelligence; machine learning; information retrieval; applications; multimodal information extraction; image-aided information extraction; image information extraction.

Prof. Dr. Ming Liu
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

• text mining
• knowledge discovery in text
• content analysis
• text analysis
• text classification
• audio-to-text mining
• video-to-text mining
• image-to-text mining
• big data
• data mining
• artificial intelligence
• machine learning
• information retrieval
• applications
• multimodal information extraction
• image-aided information extraction
• image information extraction