Journal on Data Mining and Information Retrieval (JDMIR) (ISSN: 2287-6545)

Description

We are living the world of information surplus, and almost every important aspect of our life is tracked in computer records. Those records can be utilized and useful patterns can be derived if we use the data mining and information retrieval techniques. For example, indexing, retrieval, management and mining of abundant text data on the web or digital library have become very important nowadays. The large number of text documents and the lack of formal structure in the natural-language narrative make the text search and processing very difficult, thus it is essential to develop efficient and effective text searching, retrieval and mining techniques from this ever-expanding collection of text data. Recently data mining has been successfully applied to a number of information retrieval tasks, such as statistical inference, machine learning and information retrieval, supervised learning and its application to text classification, unsupervised/semi-supervised learning, and its applications to collaborative filtering and text clustering. In these applications, data mining models are able to assist the process of information retrieval more efficiently and effectively. These include providing information retrieval process patterns that are found by the data mining models. Industry practitioners have realized that using data mining techniques in information retrieval will result in clear benefits and enhance information retrieval. Many large enterprises are believed to have been using data mining techniques in their online businesses, such as including a list of recommendations when a web user is browsing or searching for a particular product. Many of the ranking systems in information retrieval also gain performance benefits when adopting certain data mining techniques, including clustering, association patterns, etc.
The goal of the journal is to encourage researchers and practitioners from these two disciplines to address some of these challenges, help cross fertilization of ideas, and provide a common forum for the exchange of ideas in an informal environment. This journal on Data Mining and Information Retrieval focuses on the latest research work in the interrelationship between data mining, information retrieval and information extraction, new methods, techniques that take advantages of the full mutual benefits of the three components in the same framework, topics include but not limit to:.
SCOPE OF THE JOURNAL
Topics of interest include, but are not limited to:
? Traditional technologies of Knowledge and Data
? Latest Theories and Models for Information Retrieval
? New Improvements in Efficiency and Performance of Information Retrieval
? Evaluation and Test of Text Collections, Evaluation methods and metrics, Experimental design,
Data collection and analysis methods
? Indexing, Query representation, Query reformulation, Structure-based representation, XML, Metadata, and Summarization
? Natural language processing and representation for IR and Data Mining
? Tracking, Filtering, Topic detection, Collaborative filtering, Agents, Routing and Email spam
? Text Categorization and Clustering
? Text Data Mining and Machine Learning for IR
? Cross-language and multilingual Information Retrieval
? Text content representation (Indexing), Structure-based representation, XML, Metadata, Request representation, Queries,
and Summarization
? Web IR and Digital Libraries
? Machine Translation for IR and Data Mining
? Topic detection and tracking, Content-based filtering, Collaborative filtering, Agents, Routing, Email spam
? Question Answering and Extraction: Question answering, Information extraction, Lexical acquisition
? Domain Specific IR Applications: Genomic IR, IR for chemical structures, etc
? Information Retrieval and Data mining applications in bioinformatics, electronic commerce, Web, intrusion detection, finance,
marketing, healthcare
? Data mining models: Statistical techniques for generation of a robust, consistent data model
? Declarative/algebraic languages for data mining: Integration of database languages such as SQL and XML with data mining,
Coupling between database, data warehouse and data mining systems
? Post-processing, data transformations: Incremental mining and knowledge-base refinement, Foundational concepts for
exploratory data analysis, Model scoring, meta learning, meta-data model management, Privacy preserving data mining models and
algorithms
? Optimization techniques
? Multimedia and multimodal information access and retrieval: Content-based information