ChemTextMiner: An open source tool kit for mining medical literature abstracts

Text mining involves recognizing patterns from a wealth of information hidden latent in unstructured text and deducing explicit relationships among data entities by using data mining tools. Text mining of Biomedical literature is essential for building biological network connecting genes, proteins, drugs, therapeutic categories, side effects etc. related to diseases of interest. We present an approach for textmining biomedical literature mostly in terms of not so obvious hidden relationships and build biological network applied for the textmining of important human diseases like MTB, Malaria, Alzheimer and Diabetes. The methods, tools and data used for building biological networks using a distributed computing environment previously used for ChemXtreme[1] and ChemStar[2] applications are also described

DATA MINING LANGUAGES STANDARDS

The increasing of the database dimension creates many problems, especially when we need to access, use and analyze data. The data overflow phenomenon in database environments imposes the application of different data mining methods, in order to find relevant information from large databases. A lot of data mining tools emerged in the last years. The standardization of data mining languages become in the last years a very important topic. The paper presents Predictive Model Markup Language (PMML) standards from the Data Mining Group. PMML, a standard language for defining data mining models, which allows users to develop models within one vendor's application, and use other vendors' applications to visualize, analyze, evaluate or otherwise use the models.

NCeSS Project : Data mining for social scientists

We will discuss the work being undertaken on the NCeSS data mining project, a one year project at the University of Manchester which began at the start of 2007, to develop data mining tools of value to the social science community. Our primary goal is to produce a suite of data mining codes, supported by a web interface, to allow social scientists to mine their datasets in a straightforward way and hence, gain new insights into their data. In order to fully define the requirements, we are looking at a range of typical datasets to find out what forms they take and the applications and algorithms that will be required. In this paper, we will describe a number of these datasets and will discuss how easily data mining techniques can be used to extract information from the data that would either not be possible or would be too time consuming by more standard methods

Research Phases of University Data Mining Project Development

Educational Data Mining becomes one of the challenging new research fields where data mining methods and tools could help universities in taking timely and data analysis based management decisions, thus contributing to gaining competitive advantages in their successful policy introduction. This paper presents the research activities performed for the implementation of a data mining project initiated in one of the most prestigious Bulgarian universities. The project main goal is to reveal the high potential of data mining applications for university management, referring to the optimal usage of data mining methods and techniques to deeply analyze the collected historical data. That will lead to better understanding the student behavior and building well structured educational process that meets the university policy and supports the management decision making process

ASquare: A powerful evaluation tool for adaptive hypermedia course system

Currently many methods and tools are being developed to support e-Learning courses. On the one hand, they are used to help students. On the other, a few applications are being developed to help course designers and instructors. In addition, the development of this applications is important for improving the performance of the course. Thus, we proposed in this paper to use data mining methods to aid in the designing of adaptive courses and the evaluation of their effectiveness. Lastly, the results of the implementation of our tool and examples of the utility of Data Mining for teachers is given

Binary Particle Swarm Optimization based Biclustering of Web usage Data

Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms