These are all syntactic properties that together represent already defined categories, concepts, senses or meanings [7]. Text mining must recognize, extract and use the information. Instead of searching for words, we can search for semantic patterns, and this is therefore searching at a higher level. Text mining involves a series of activities to be performed in order to efficiently mine the information. These activities are:. Text Cleanup means removing of any unnecessary or unwanted information such as remove ads from web pages, normalize text converted from binary formats, deal with tables, figures and formulas.
Tokenizing is simply achieved by splitting the text on white spaces and at punctuation marks that do not belong to abbreviations identified in the preceding step.
Part-of-Speech POS tagging means word class assignment to each token. Its input is given by the tokenized text. Taggers have to cope with unknown words OOV problem and ambiguous word-tag mappings. Rule-based approaches like ENGTWOL [8] operate on a dictionaries containing word forms together with the associated POS labels and morphological and syntactic features and b context sensitive rules to choose the appropriate labels during application.
A text document is represented by the words features it contains and their occurrences. Two main approaches of document representation are a Bag of words b Vector Space. Feature selection also known as variable selection, is the process of selecting a subset of important features for use in model creation. The main assumption when using a feature selection technique is that the data contain many redundant or irrelevant features.
Redundant features are the one which provides no extra information. Irrelevant features provide no useful or relevant information in any context. Feature selection technique is a subset of the more general field of feature extraction. At this point the Text mining process merges with the traditional Data Mining process. Classic Data Mining techniques are used in the structured database that resulted from the previous stages. Evaluate the result, after evaluation the result can be discarded or the generated result can be used as an input for the next set of sequence.
These days web contains a treasure of information about subjects such as persons, companies, organizations, products, etc. Web Mining is an application of data mining techniques to discover hidden and unknown patterns from the Web.
Web mining is an activity of identifying term implied in large document collection say C, which can be denoted by a mapping i. The first step toward any Web-based text mining effort would be to gather a substantial number of web pages having mention of a subject.
Thus, the challenge becomes not only to find all the subject occurrences, but also to filter out those that have the desired meaning. Everyone wants to understand specific diseases what they have , to be informed about new therapies, ask for a second opinion before one can decide a treatment. E-mails, e-consultations, and requests for medical advice via the Internet have been manually analyzed using quantitative or qualitative methods [12]. So, specific requests could be directed to the expert or even answered semi-automatically, thereby providing complete monitoring.
Machine-based analyses could help both the public to better handle the mass of information and medical experts to give expert feedback. An automatic classification of amateur requests to medical expert internet forums is a challenging task because these requests can be very long and unstructured as a result of mixing, for example, personal experiences with laboratory data. Big enterprises and headhunters receive thousands of resumes from job applicants every day. Extracting information from resumes with high precision and recall is not an easy task [1].
In spite of constituting a restricted domain, resumes can be written in a multitude of formats e. Link and Graph Mining l. Semantic-based Data Mining and Data Pre-processing m. Mobility and Big Data n.
Intrusion Detection for Gigabit Networks b. High Performance Cryptography d. Visualizing Large Scale Security Data e. Threat Detection using Big Data Analytics f. Privacy Threats of Big Data g. User Studies for any of the above j. Sociological Aspects of Big Data Privacy 6. Big Data Applications a. Big Data as a Service f. Big Data Industry Standards g. The focus of industry track is on papers that address the practical, applied, or pragmatic or new research challenge issues related to the use of Big Data in industry.
We accept full papers up to 10 pages and extended abstracts pages. Conference Co-Chairs: Prof. Vijay Raghavan, Univ. Benjamin Wah, Chinese Univ. Ricardo Baeza-Yates, Yahoo! Labs, Spain Prof. This knowledge can be used to guide and optimize any new business strategy implemented by the institution.
Kamberm Data mining: concepts and techniques. San Francisco: Jossey-Bass. Generalization and decision tree induction: efficient classification in data mining. Educational Data Mining: a Case Study. LOOI, G. Discovering enrollment knowledge in university databases. In KDD, pp. Data mining, knowledge management in higher education, potential applications. In workshop associate of institutional research international conference, Toronto, pp.
An academic decision-support system based on academic performance evaluation for student and program assessment, International Journal of Engineering Education, Vol. Using genetic algorithms for data mining optimizing in an educational web-based system. Adaptive decision support for academic course scheduling using intelligent software agents.
Modeling multidimensional databases. IBM Research Report. Piatetsky-Shapiro and W. Frawley, editors, Knowledge Discovery in Databases, pp. Management information Systems. Prentice Hall; 11th edition.
Vol 1 ,No 2. The tremendous growth of unlabeled data has made incremental learning take up a big leap. Starting from BI applications to image classifications, from analysis to predictions, every domain needs to learn and update. Incremental learning allows to explore new areas at the same time performs knowledge amassing.
In this paper we discuss the areas and methods of incremental learning currently taking place and highlight its potentials in aspect of decision making. The paper essentially gives an overview of the current research that will provide a background for the students and research scholars about the topic. Lui, J. Cai, J. Yin, A. Fahim, G. Saake, A. Salem, F.
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