Survey: Data Mining Techniques in Medical Data Field

Now days most of the research area are working on data mining techniques in medical data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, decision tree, machine learning algorithms like Support Vector Machines, Principle Component Analysis etc., Rough Set Theory and Fuzzy logic. In particular we consider a case study using classification techniques on a medical data set of diabetic patients

Rough approximation quality revisited

AbstractIn rough set theory, the approximation quality γ is the traditional measure to evaluate the classification success of attributes in terms of a numerical evaluation of the dependency properties generated by these attributes. In this paper we re-interpret the classical γ in terms of a classic measure based on sets, the Marczewski–Steinhaus metric, and also in terms of “proportional reduction of errors” (PRE) measures. We also exhibit infinitely many possibilities to define γ-like statistics which are meaningful in situations different from the classical one, and provide tools to ascertain the statistical significance of the proposed measures, which are valid for any kind of sample

Reducing the Memory Size of a Fuzzy Case-Based Reasoning System Applying Rough Set Techniques

Early work on case-based reasoning (CBR) reported in the literature shows the importance of soft computing techniques applied to different stages of the classical four-step CBR life cycle. This correspondence proposes a reduction technique based on rough sets theory capable of minimizing the case memory by analyzing the contribution of each case feature. Inspired by the application of the minimum description length principle, the method uses the granularity of the original data to compute the relevance of each attribute. The rough feature weighting and selection method is applied as a preprocessing step prior to the generation of a fuzzy rule system, which is employed in the revision phase of the proposed CBR system. Experiments using real oceanographic data show that the rough sets reduction method maintains the accuracy of the employed fuzzy rules, while reducing the computational effort needed in its generation and increasing the explanatory strength of the fuzzy rules

Superpixel-based roughness measure for multispectral satellite image segmentation

The new generation of artificial satellites is providing a huge amount of Earth observation images whose exploitation can report invaluable benefits, both economical and environmental. However, only a small fraction of this data volume has been analyzed, mainly due to the large human resources needed for that task. In this sense, the development of unsupervised methodologies for the analysis of these images is a priority. In this work, a new unsupervised segmentation algorithm for satellite images is proposed. This algorithm is based on the rough-set theory, and it is inspired by a previous segmentation algorithm defined in the RGB color domain. The main contributions of the new algorithm are: (i) extending the original algorithm to four spectral bands; (ii) the concept of the superpixel is used in order to define the neighborhood similarity of a pixel adapted to the local characteristics of each image; (iii) and two new region merged strategies are proposed and evaluated in order to establish the final number of regions in the segmented image. The experimental results show that the proposed approach improves the results provided by the original method when both are applied to satellite images with different spectral and spatial resolutions

Automatic message annotation and semantic interface for context aware mobile computing

This thesis was submitted for the degree of Docter of Philosophy and awarded by Brunel University.In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the device’s file system and the message header information which is then accumulated with the message’s tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved “Contextual Ontology based Short Text Messages reasoning (SOIM)”. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.The Ministry of Higher Education and Scientific Research (IRAQ

Automatic message annotation and semantic interface for context aware mobile computing

In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the device’s file system and the message header information which is then accumulated with the message’s tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved “Contextual Ontology based Short Text Messages reasoning (SOIM)”. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.EThOS - Electronic Theses Online ServiceMinistry of Higher Education and Scientific Research (Iraq)GBUnited Kingdo

Variable precision rough set theory decision support system: With an application to bank rating prediction

This dissertation considers, the Variable Precision Rough Sets (VPRS) model, and its development within a comprehensive software package (decision support system), incorporating methods of re sampling and classifier aggregation. The concept of /-reduct aggregation is introduced, as a novel approach to classifier aggregation within the VPRS framework. The software is applied to the credit rating prediction problem, in particularly, a full exposition of the prediction and classification of Fitch's Individual Bank Strength Ratings (FIBRs), to a number of banks from around the world is presented. The ethos of the developed software was to rely heavily on a simple 'point and click' interface, designed to make a VPRS analysis accessible to an analyst, who is not necessarily an expert in the field of VPRS or decision rule based systems. The development of the software has also benefited from consultations with managers from one of Europe's leading hedge funds, who gave valuable insight, advice and recommendations on what they considered as pertinent issues with regards to data mining, and what they would like to see from a modern data mining system. The elements within the developed software reflect each stage of the knowledge discovery process, namely, pre-processing, feature selection, data mining, interpretation and evaluation. The developed software encompasses three software packages, a pre-processing package incorporating some of the latest pre-processing and feature selection methods a VPRS data mining package, based on a novel "vein graph" interface, which presents the analyst with selectable /-reducts over the domain of / and a third more advanced VPRS data mining package, which essentially automates the vein graph interface for incorporation into a re-sampling environment, and also implements the introduced aggregated /-reduct, developed to optimise and stabilise the predictive accuracy of a set of decision rules induced from the aggregated /-reduct

Statistical evaluation of rough set dependency analysis

Rough set data analysis (RSDA) has recently become a frequently studied symbolic method in data mining. Among other things, it is being used for the extraction of rules from databases; it is, however, not clear from within the methods of rough set analysis, whether the extracted rules are valid. In this paper, we suggest to enhance RSDA by two simple statistical procedures, both based on randomization techniques, to evaluate the validity of prediction based on the approximation quality of attributes of rough set dependency analysis. The first procedure tests the casualness of a prediction to ensure that the prediction is not based on only a few (casual) observations. The second procedure tests the conditional casualness of an attribute within a prediction rule. The procedures are applied to three data sets, originally published in the context of rough set analysis. We argue that several claims of these analyses need to be modified because of lacking validity, and that other possibly significant results were overlooked