Data Mining Functions Research Articles

Hybrid Perturbation Technique using Feature Selection Method for Privacy Preservation in Data Mining

preserving in data mining refers to the area of data mining that seeks to safeguard sensitive information from unsolicited or unsanctioned disclosure and hence protecting individual data records and their privacy. Data perturbation is a privacy preservation technique which does addition / multiplication of noise to the original data. It performs anonymization based on the data type of sensitive data. Generalization is a technique were quasi identifiers data are replaced by some other more general term. In this paper privacy protection is applied to high dimensional datasets like Adult and Census. For ranking the attributes, information gain feature subset selection method is used. The high ranking attributes with sensitive information are set as quasi identifiers of the datasets. A hybrid perturbation technique is used to perturb categorical and numeric attributes of both the datasets and the utility of the datasets is measured using accuracy on data mining functionalities. The data distortion is measured using maintenance of Rank of Features (CK) between the original and perturb datasets. Experimental results show that utility of the perturbed datasets comparable with the original dataset and the Census dataset has comparable CK value than adult dataset.

Data Mining and Its Applications for Knowledge Management : A Literature Review from 2007 to 2012

Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.

Redescription Mining With Three Primary Data Mining Functionalities

Describing an object in two ways or shifting the vocabulary of the same concept is Redescription. Not a new problem, Redescription Mining premise had resulted the subsets of objects that afford multiple definitions, in a given Universal set of the same, and a collection of features to describe them. Now-a-days, huge amounts of data available either to classify or to categorize leads us to ambiguous state as it is accomplished with complementary and contradictory ways. Hence data has to be reduced. This involves cataloging, classification, identifying rules among the data, segmentation or partitioning of the data. The Learning algorithms of data mining techniques on this data can often be viewed as a further form of data reduction. This Sine-qua-non data has been characterized by the multitude of descriptors. In a way, these descriptors are also made equivalent and hence reduced. The methodology of redescriptions can be obtained in scores of data mining techniques. In this paper we overview how data mining functionalities like classification, clustering and Association rule mining achieve the goal of redecsriptions.

Traffic Accident Data Profiling and Clusteringwith Data Mining Process

Some of this traffic accident crisis in Nigeria is caused by the increase in number of vehicles and inefficient drivers on the road, bad condition and poor maintenance of the roads. The significance of the study lies on the profiling of clusters of traffic roads in terms of accident related data and the degree in which these accident characteristics are perceptive between the different created clusters. Applying data mining process to model traffic accident data records helped in obtaining the characteristics of drivers' behaviour, road condition and weather condition that are connected with different injury severities and death. The traffic roads are divided into a low accident risk and high accident risk traffic roads, determining accidents in different age categories and period of accidents. A design of a data mining model for analysis and prediction of accidents rate in Nigeria was presented. In this study, we profiled traffic roads, differentiated the data set into pre- processing and transforming data set; created the association rules; and post-processed the frequent accident item sets. The data mining function was used and data cleaned using feature selection.

SD-miner System to Retrieve Probabilistic Neighborhood Points in Spatial Data Mining

In GIS or Geographic Information system technology, a vast volume of spatial data has been accumulated, thereby incurring the necessity of spatial data mining techniques. Displaying and visualizing such data items are important aspects. But no RDBMS software is loaded with displaying the spatial result over a MAP overlay or answer spatial queries like all the points within certain Neighborhood. In this project, we propose a new spatial data mining system named SD-Miner. SD-Miner consists of three parts: A Graphical User Interface for inputs and outputs, a Data Mining Module that processes spatial data mining functionalities, a Data Storage Model that stores and manages spatial as well as non-spatial data by using a DBMS. In particular, the data mining module provides major spatial data mining functionalities such as spatial clustering, spatial classification, spatial characterization, and spatio-temporal association rule mining. SD-Miner has its own characteristics: (1) It supports users to perform non-spatial data mining functionalities as well as spatial data mining functionalities intuitively and effectively. (2) It provides users with spatial data mining functions as a form of libraries, thereby making applications conveniently use those functions. (3) It inputs parameters for mining as a form of database tables to increase flexibility. Result shows that significantly reduced and precise data items are displayed through the result of this technique.

F011004 Isightにおけるデータマイニングと意思決定技術

Basic features of Isight and its data-mining functions and surrogates are presented.Data-mining has an unique importance in engineering optimization that arises from black-box nature of the functions to be evaluated.Data-mining and problem analyses encourage efficiency and sanity of optimization tasks.A number of related methods in Isight are organized in the four categories : "Optimize","Visualize,Analyze","Deterministic,Theoretic","Stochastic,Heuristic".Then basic templates of conducting data-mining and problem analyses are introduced by means of the portfolio.

Applying Data Mining to the Geosciences Data

Abstract The article detailedly addresses the features of the petrophysical data, logging data, seismic data and geological data based on the concepts of the data mining. The mining ideas regarding the petrophysical and logging data, seismic data and geological data are made based on their features. The article uses different mining ways to process the corresponding data, and describes the results from the perspective of the functions of data mining. According to the data mining techniques, the petrophysical data are applied to find the relations and forecast reservoirs; the logging data will be employed to evaluate the fuzzy reservoirs and recognize the effective reservoirs in complicated geological conditions; the space mining results of the 3D seismic data; the charts and text mining results of the geological data. The oil and natural gas data mining in the exploration adopts the methods of data analysis and the corresponding mathematical model to process the exploration data, and get the potential information. It has realized the purpose that the data guide exploration and given the concept of data exploration.

MoveMine

With the maturity and wide availability of GPS, wireless, telecommunication, and Web technologies, massive amounts of object movement data have been collected from various moving object targets, such as animals, mobile devices, vehicles, and climate radars. Analyzing such data has deep implications in many applications, such as, ecological study, traffic control, mobile communication management, and climatological forecast. In this article, we focus our study on animal movement data analysis and examine advanced data mining methods for discovery of various animal movement patterns. In particular, we introduce a moving object data mining system, MoveMine, which integrates multiple data mining functions, including sophisticated pattern mining and trajectory analysis. In this system, two interesting moving object pattern mining functions are newly developed: (1) periodic behavior mining and (2) swarm pattern mining . For mining periodic behaviors, a reference location-based method is developed, which first detects the reference locations, discovers the periods in complex movements, and then finds periodic patterns by hierarchical clustering. For mining swarm patterns, an efficient method is developed to uncover flexible moving object clusters by relaxing the popularly-enforced collective movement constraints. In the MoveMine system, a set of commonly used moving object mining functions are built and a user-friendly interface is provided to facilitate interactive exploration of moving object data mining and flexible tuning of the mining constraints and parameters. MoveMine has been tested on multiple kinds of real datasets, especially for MoveBank applications and other moving object data analysis. The system will benefit scientists and other users to carry out versatile analysis tasks to analyze object movement regularities and anomalies. Moreover, it will benefit researchers to realize the importance and limitations of current techniques and promote future studies on moving object data mining. As expected, a mastery of animal movement patterns and trends will improve our understanding of the interactions between and the changes of the animal world and the ecosystem and therefore help ensure the sustainability of our ecosystem.

GWAS Integrator: a bioinformatics tool to explore human genetic associations reported in published genome-wide association studies

Genome-wide association studies (GWAS) have successfully identified numerous genetic loci that are associated with phenotypic traits and diseases. GWAS Integrator is a bioinformatics tool that integrates information on these associations from the National Human Genome Research institute (NHGRI) Catalog, SNAP (SNP Annotation and Proxy Search), and the Human Genome Epidemiology (HuGE) Navigator literature database. This tool includes robust search and data mining functionalities that can be used to quickly identify relevant associations from GWAS, as well as proxy single-nucleotide polymorphisms (SNPs) and potential candidate genes. Query-based University of California Santa Cruz (UCSC) Genome Browser custom tracks are generated dynamically on the basis of users' selected GWAS hits or candidate genes from HuGE Navigator literature database (http://www.hugenavigator.net/HuGENavigator/gWAHitStartPage.do). The GWAS Integrator may help enhance inference on potential genetic associations identified from GWAS studies.

OWLIM: A family of scalable semantic repositories

An explosion in the use of RDF for representing information about resources has driven the requirements for Web-scale server systems that can store and process huge quantities of data, and furthermore provide powerful data access and mining functionalities. This paper describes OWLIM, a family of semantic repositories that provide storage, inference and novel data-access features delivered in a scalable, resilient, industrial-strength platform.

Mining and Analysing Spatio-Temporal Patterns of Gene Expression in An Integrative database Framework

SummaryMining patterns of gene expression provides a crucial approach in discovering knowledge such as finding genetic networks that underpin the embryonic development. Analysis of mining results and evaluation of their relevance in the domain remains a major concern. In this paper we describe our explorative studies in support of solutions to facilitate the analysis and interpretation of mining results. In our particular case we describe a solution that is found in the extension of the Gene Expression Management System (GEMS), i.e. an integrative framework for spatio-temporal organization of gene expression patterns of zebrafish to a framework supporting data mining, data analysis and patterns interpretation As a proof of principle, the GEMS has been equipped with data mining functionality suitable for spatio-temporal tracking, thereby generating added value to the submission of data for data mining and analysis. The analysis of the genetic networks is based on the availability of domain ontologies which dynamically provides meaning to the discovered patterns of gene expression data. Combination of data mining with the already presently available capabilities of GEMS will significantly augment current data processing and functional analysis strategies

Remote Health Monitoring of Heart Failure With Data Mining via CART Method on HRV Features

Disease management programs, which use no advanced information and computer technology, are as effective as telemedicine but more efficient because less costly. We proposed a platform to enhance effectiveness and efficiency of home monitoring using data mining for early detection of any worsening in patient's condition. These worsenings could require more complex and expensive care if not recognized. In this letter, we briefly describe the remote health monitoring platform we designed and realized, which supports heart failure (HF) severity assessment offering functions of data mining based on the classification and regression tree method. The system developed achieved accuracy and a precision of 96.39% and 100.00% in detecting HF and of 79.31% and 82.35% in distinguishing severe versus mild HF, respectively. These preliminary results were achieved on public databases of signals to improve their reproducibility. Clinical trials involving local patients are still running and will require longer experimentation.

WRAPPER-BASED FEATURE RANKING TECHNIQUES FOR DETERMINING RELEVANCE OF SOFTWARE ENGINEERING METRICS

Classification, an important data mining function that assigns class label to items in a collection, is of practical applications in various domains. In software engineering, for instance, a common classification problem is to determine the quality of a software item. In such a problem, software metrics represent the independent features while the fault proneness represents the class label. With many classification problems, one must often deal with the presence of irrelevant features in the feature space. That, coupled with class imbalance, renders the task of discriminating one class from another rather difficult. In this study, we empirically evaluate our proposed wrapper-based feature ranking where nine performance metrics aided by a particular learner and a methodology are considered. We examine five learners and take three different approaches, each in conjunction with one of three different methodologies: 3-fold Cross-Validation, 3-fold Cross-Validation Risk Impact, and a combination of the two. In this study, we consider two sets of software engineering datasets. To evaluate the classifier performance after feature selection has been applied, we use Area Under Receiver Operating Characteristic curve as the performance evaluator. We investigate the performance of feature selection as we vary the three factors that form the foundation of the wrapper-based feature ranking. We show that the performance is conditioned by not only the choice of methodology but also the learner. We also evaluate the effect of sampling on wrapper-based feature ranking. Finally, we provide guidance as to which software metrics are relevant in software defect prediction problems and how the number of software metrics can be selected when using wrapper-based feature ranking.

The Application of CPFR Model in Information System

As a new supply chain strategic technology, CPFR stresses the importance of cooperation theory and the partnership in supply chain strategies. But in reality, the application process is not as satisfactory as forecasted in theory. The problems include the complexity of the nine step decrease the efficiency of the system, the demand is not taken important enough here and there are still no very clear solution for the sales and order forecast steps. And the data mining function added to the system in this paper is still a structural design and the detailed designs need to be worked out before this function can be used in reality.

Mining and analysing spatio-temporal patterns of gene expression in an integrative database framework.

Mining patterns of gene expression provides a crucial approach in discovering knowledge such as finding genetic networks that underpin the embryonic development. Analysis of mining results and evaluation of their relevance in the domain remains a major concern. In this paper we describe our explorative studies in support of solutions to facilitate the analysis and interpretation of mining results. In our particular case we describe a solution that is found in the extension of the Gene Expression Management System (GEMS), i.e. an integrative framework for spatio-temporal organization of gene expression patterns of zebrafish to a framework supporting data mining, data analysis and patterns interpretation As a proof of principle, the GEMS has been equipped with data mining functionality suitable for spatio-temporal tracking, thereby generating added value to the submission of data for data mining and analysis. The analysis of the genetic networks is based on the availability of domain ontologies which dynamically provides meaning to the discovered patterns of gene expression data. Combination of data mining with the already presently available capabilities of GEMS will significantly augment current data processing and functional analysis strategies.

Clinical Data Mining: a Review

Clinical data mining is the application of data mining techniques using clinical data. We review the literature in order to provide a general overview by identifying the status-of-practice and the challenges ahead. The nine data mining steps proposed by Fayyad in 1996 [4] were used as the main themes of the review. MEDLINE was used as primary source and 84 papers were retained based on our inclusion criteria. Clinical data mining has three objectives: understanding the clinical data, assist healthcare professionals, and develop a data analysis methodology suitable for medical data. Classification is the most frequently used data mining function with a predominance of the implementation of Bayesian classifiers, neural networks, and SVMs (Support Vector Machines). A myriad of quantitative performance measures were proposed with a predominance of accuracy, sensitivity, specificity, and ROC curves. The latter are usually associated with qualitative evaluation. Clinical data mining respects its commitment to extracting new and previously unknown knowledge from clinical databases. More efforts are still needed to obtain a wider acceptance from the healthcare professionals and for generalization of the knowledge and reproducibility of its extraction process: better description of variables, systematic report of algorithm parameters including the method to obtain them, use of easy-to-understand models and comparisons of the efficiency of clinical data mining with traditional statistical analyses. More and more data will be available for data miners and they have to develop new methodologies and infrastructures to analyze the increasingly complex medical data.

Discovery: an interactive resource for the rational selection and comparison of putative drug target proteins in malaria

BackgroundUp to half a billion human clinical cases of malaria are reported each year, resulting in about 2.7 million deaths, most of which occur in sub-Saharan Africa. Due to the over-and misuse of anti-malarials, widespread resistance to all the known drugs is increasing at an alarming rate. Rational methods to select new drug target proteins and lead compounds are urgently needed. The Discovery system provides data mining functionality on extensive annotations of five malaria species together with the human and mosquito hosts, enabling the selection of new targets based on multiple protein and ligand properties.MethodsA web-based system was developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein features used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions and host-pathogen interactions among others. Searching by chemical structure is also available.ResultsAn in silico system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from Plasmodium falciparum.ConclusionThe Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties.

DATA MINING AND ITS APPLICATIONS FOR MODELLING RAINFALL EXTREMES

ABSTRACT Data mining is a new powerful technology which helps in extracting hidden predictive information (future trends and behaviours) from large databases and thus facilitating decision makers to make proactive, knowledge-driven decisions. In this paper, a brief overview of various data mining functionalities, and an extensive review of the works done on temporal data mining are discussed. Of the two frameworks of temporal data mining, one that of frequent episodes is discussed in detail by explicating the various algorithms developed so far. Also, a case study using one of the algorithms, Minimal Occurrences With Constraints And Time Lags (MOWCATL), for extracting the rules to explain the spatial and temporal variation for extreme events in India is discussed and the results are shown.

Intelligent Frame work for Increasing Life Time of Peer to Peer networks

In a peer-to-peer network each computer acts as both a server and a client—supplying and receiving files—with bandwidth and processing distributed among all members of the network. Such a decentralized network uses resources more efficiently than a traditional network and is less vulnerable to systemic failure. Peer to Peer networks are used by Bluetooth electronics and Internet based communication services, but development has largely been driven by online file sharing. The existence of high degree of malicious peers is a serious threat to the P2P network life time. This paper we propose an intelligent framework which deals with the issue of identifying free riders and other kind of peers who reduces the networks life time. Additionally, this paper also proposes a basic data warehouse structure for a peer to peer system and analyzes peer behavior using data mining functionalities. Results prove that the free riders and malicious peers get automatically eliminated from the system thereby increasing the networks life time.

Information Security: An Artificial Intelligence and Data Mining Based Approach

Information security plays a major role in case of secured data transmission . In this paper some intelligent techniques have been pointed out regarding effective cipher generation based on SKEY , interlock protocol and SKID . We have also cited cryptosystems based on linear property of propositional operator, the concept of fuzzy logic , optimized function and concept of data mining based on comparison analysis. Various proposed techniques have also been pointed out for shared key evalution in multi-party communication and the areas concerned are shared key generation based on support rule, cent percent confidence rule ,minimal frequent set and sequence mining and feature-based method.