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Information
Special Issues
Intelligent Data Analysis
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Intelligent Data Analysis

A special issue of Information (ISSN 2078-2489).

Deadline for manuscript submissions: closed (20 December 2014)

Special Issue Editor

Prof. Dr. Min Yao

E-Mail Website
Guest Editor
College of Computer Science & Technology, Zhejiang University, Hangzhou 310027, China
Interests: computational intelligence; data mining; machine learning; pattern recognition; knowledge management

Special Issue Information

Dear Colleagues,

Intelligent Data Analysis (IDA) is one of the hot issues in the field of artificial intelligence and information. Intelligent data analysis reveals implicit, previously unknown and potentially valuable information or knowledge from large amounts of data. Intelligent data analysis is also a kind of decision support process. Based on artificial intelligence, machine learning, pattern recognition, statistics, database and visualization technology mainly, IDA automatically extracts useful information, necessary knowledge and interesting models from a lot of online data in order to help decision makers make the right choices.

The process of IDA generally consists of the following three stages: (1) data preparation; (2) rule finding or data mining; (3) result validation and explanation. Data preparation involves selecting the required data from the relevant data source and integrating this into a data set to be used for data mining. Rule finding is working out rules contained in the data set by means of certain methods or algorithms. Result validation requires examining these rules, and result explanation is giving intuitive, reasonable and understandable descriptions using logical reasoning.

As the goal of intelligent data analysis is to extract useful knowledge, the process demands a combination of extraction, analysis, conversion, classification, organization, reasoning, and so on. It is challenging and fun working out how to choose appropriate methods to resolve the difficulties encountered in the process. Intelligent data analysis methods and tools, as well as the authenticity of obtained results pose us continued challenges.

The goal of this Special Issue is to provide the interested reader with a collection of papers describing recent developments in intelligent data analysis. Topics of interest include, but are not limited to:

  • Theory and model
  • Algorithm and simulation
  • Feature extraction
  • Parallel and distributed data analysis
  • Big data analysis
  • Image analysis
  • Multimedia analysis
  • Complex data analysis
  • Web information analysis
  • Economic data analysis
  • Biomedical data analysis
  • Medical informatics

Prof. Dr. Min Yao
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. Information 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 1600 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.


Published Papers (3 papers)

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Research

318 KiB  
Article
Graph Regularized Within-Class Sparsity Preserving Projection for Face Recognition
by Songjiang Lou, Xiaoming Zhao, Wenping Guo and Ying Chen
Information 2015, 6(2), 152-161; https://0-doi-org.brum.beds.ac.uk/10.3390/info6020152 - 24 Apr 2015
Cited by 1 | Viewed by 5126
Abstract
As a dominant method for face recognition, the subspace learning algorithm shows desirable performance. Manifold learning can deal with the nonlinearity hidden in the data, and can project high dimensional data onto low dimensional data while preserving manifold structure. Sparse representation shows its [...] Read more.
As a dominant method for face recognition, the subspace learning algorithm shows desirable performance. Manifold learning can deal with the nonlinearity hidden in the data, and can project high dimensional data onto low dimensional data while preserving manifold structure. Sparse representation shows its robustness for noises and is very practical for face recognition. In order to extract the facial features from face images effectively and robustly, in this paper, a method called graph regularized within-class sparsity preserving analysis (GRWSPA) is proposed, which can preserve the within-class sparse reconstructive relationship and enhances separatability for different classes. Specifically, for each sample, we use the samples in the same class (except itself) to represent it, and keep the reconstructive weight unchanged during projection. To preserve the manifold geometry structure of the original space, one adjacency graph is constructed to characterize the interclass separability and is incorporated into its criteria equation as a constraint in a supervised manner. As a result, the features extracted are sparse and discriminative and helpful for classification. Experiments are conducted on the two open face databases, the ORL and YALE face databases, and the results show that the proposed method can effectively and correctly find the key facial features from face images and can achieve better recognition rate compared with other existing ones. Full article
(This article belongs to the Special Issue Intelligent Data Analysis)
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1177 KiB  
Article
Analysis and Visualization for Hot Spot Based Route Recommendation Using Short-Dated Taxi GPS Traces
by Ying Shen, Ligang Zhao and Jing Fan
Information 2015, 6(2), 134-151; https://0-doi-org.brum.beds.ac.uk/10.3390/info6020134 - 21 Apr 2015
Cited by 43 | Viewed by 7599
Abstract
Taxi GPS traces, which contain a great deal of valuable information as regards to human mobility and city traffic, can be extracted to improve the quality of our lives. Since the method of visualized analysis is believed to be an effective way to [...] Read more.
Taxi GPS traces, which contain a great deal of valuable information as regards to human mobility and city traffic, can be extracted to improve the quality of our lives. Since the method of visualized analysis is believed to be an effective way to present information vividly, we develop our analysis and visualization method based on a city’s short-dated taxi GPS traces, which can provide recommendation to help cruising taxi drivers to find potential passengers with optimal routes. With our approach, hot spots for loading and unloading passenger(s) are extracted using an improved DBSCAN algorithm after data preprocessing including cleaning and filtering. Then, this paper describes the start-end point-based similar trajectory method to get coarse-level trajectories clusters, together with the density-based ε distance trajectory clustering algorithm to identify recommended potential routes. A weighted tree is defined including such factors as driving time, velocity, distance and endpoint attractiveness for optimal route evaluation from vacant to occupied hot spots. An example is presented to show the effectiveness of our visualization method. Full article
(This article belongs to the Special Issue Intelligent Data Analysis)
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954 KiB  
Article
Modeling of Experimental Adsorption Isotherm Data
by Xunjun Chen
Information 2015, 6(1), 14-22; https://0-doi-org.brum.beds.ac.uk/10.3390/info6010014 - 22 Jan 2015
Cited by 367 | Viewed by 27370
Abstract
Adsorption is considered to be one of the most effective technologies widely used in global environmental protection areas. Modeling of experimental adsorption isotherm data is an essential way for predicting the mechanisms of adsorption, which will lead to an improvement in the area [...] Read more.
Adsorption is considered to be one of the most effective technologies widely used in global environmental protection areas. Modeling of experimental adsorption isotherm data is an essential way for predicting the mechanisms of adsorption, which will lead to an improvement in the area of adsorption science. In this paper, we employed three isotherm models, namely: Langmuir, Freundlich, and Dubinin-Radushkevich to correlate four sets of experimental adsorption isotherm data, which were obtained by batch tests in lab. The linearized and non-linearized isotherm models were compared and discussed. In order to determine the best fit isotherm model, the correlation coefficient (r2) and standard errors (S.E.) for each parameter were used to evaluate the data. The modeling results showed that non-linear Langmuir model could fit the data better than others, with relatively higher r2 values and smaller S.E. The linear Langmuir model had the highest value of r2, however, the maximum adsorption capacities estimated from linear Langmuir model were deviated from the experimental data. Full article
(This article belongs to the Special Issue Intelligent Data Analysis)
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