Mining Procedures Research Articles

Numerical simulation of scattering wave imaging in a goaf

Goafs are threats to safe mining. Their imaging effects or those of other complex geological bodies are often poor in conventional reflected wave images. Hence, accurate detection of goafs has become an important problem, to be solved with a sense of urgency. Based on scattering theory, we used an equivalent offset method to extract Common Scattering Point gathers, in order to analyze different scattering wave characteristics between Common Scattering Point and Common Mid Point gathers and to compare stack and migration imaging effects. Our research results show that the scattering wave imaging method is more efficient than the conventional imaging method and is therefore a more effective imaging method for detecting goafs and other complex geological bodies. It has important implications for safe mining procedures and infrastructures.

Expert system for the assessment of power transformer insulation condition based on type-2 fuzzy logic systems

An efficient expert system for the power transformer condition assessment is presented in this paper. Through the application of Duval’s triangle and the method of the gas ratios a first assessment of the transformer condition is obtained in the form of a dissolved gas analysis (DGA) diagnosis according IEC 60599. As a second step, a knowledge mining procedure is performed, by conducting surveys whose results are fed into a first Type-2 Fuzzy Logic System (T2-FLS), in order to initially evaluate the condition of the equipment taking only the results of dissolved gas analysis into account. The output of this first T2-FLS is used as the input of a second T2-FLS, which additionally weighs up the condition of the paper-oil system. The output of this last T2-FLS is given in terms of words easily understandable by the maintenance personnel. The proposed assessing methodology has been validated for several cases of transformers in service.

An effective tree structure for mining high utility itemsets

In the past, many algorithms were proposed to mine association rules, most of which were based on item frequency values. Considering a customer may buy many copies of an item and each item may have different profits, mining frequent patterns from a traditional database is not suitable for some real-world applications. Utility mining was thus proposed to consider costs, profits and other measures according to user preference. In this paper, the high utility pattern tree (HUP tree) is designed and the HUP-growth mining algorithm is proposed to derive high utility patterns effectively and efficiently. The proposed approach integrates the previous two-phase procedure for utility mining and the FP-tree concept to utilize the downward-closure property and generate a compressed tree structure. Experimental results also show that the proposed approach has a better performance than Liu et al.’s two-phase algorithm in execution time. At last, the numbers of tree nodes generated from three different item ordering methods are also compared, with results showing that the frequency ordering produces less tree nodes than the other two.

Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers

BackgroundThe purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School.ResultsWe found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential.ConclusionsValid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.

EvoCMAR: A New Evolutionary Method to Directly Mine Association Rules for Classification

AbstractIn this paper, we propose an evolutionary method with a three‐layer structure to directly mine association rules for classification. The association rules have been demonstrated to be useful for classification, such as classification based on association rule (CBA) and classification method based on multiple association rule (CMAR), and they are found to be more accurate than some traditional methods, such as C4.5. Generally speaking, there are two phases in an associative classification method: (i) association rules mining; (ii) classification by association rules. However, the two phases are almost separated, viz, during the first phase, the mining of association rules does not focus on classification. Moreover, when building the classifier in the second phase, most of the association rues will be pruned. As a result, if we are able to directly mine the classification association rules, we can save time. Meanwhile, we can expect even better accuracy because the mining procedure itself considers the classification. In this paper, we build a novel evolutionary method, named evolutionary classification method based on multiple association rule (EvoCMAR), to tackle these problems, and the simulation results show that it performs well in both accuracy and speed. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

To mine association rules of customer values via a data mining procedure with improved model: An empirical case study

This paper proposes a new procedure and an improved model to mine association rules of customer values. The market of online shopping industry in Taiwan is the research area. Research method adopts Ward’s method to partition online shopping market into three markets. Customer values are refined from an improved RFMDR model (based on RFM/RFMD model). Supervised Apriori algorithm is employed with customer values to create association rules. These effective rules are suggested to apply on a customized marketing function of a CRM system for enhancing their customer values to be higher grades.

An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure

The shape prior segmentation procedure and pruned association rule with ImageApriori algorithm has been used to develop an improved brain image classification system are presented in this paper. The CT scan brain images have been classified into three categories namely normal, benign and malignant, considering the low-level features extracted from the images and high level knowledge from specialists to enhance the accuracy in decision process. The experimental results on pre-diagnosed brain images showed 97% sensitivity, 91% specificity and 98.5% accuracy. The proposed algorithm is expected to assist the physicians for efficient classification with multiple key features per image.

Heart rate variability: An index of brain processing in vegetative state? An artificial intelligence, data mining study

Objective Brain processing at varying levels of functional complexity has been documented in vegetative state. In this study, data mining procedures are applied to identify significant changes in heart rate variability (an emerging objective descriptor of autonomic correlates of brain activation) in response to complex auditory stimuli with emotional value (music). Methods The heart rate of subjects in vegetative state from brain damage ( n = 6) or spontaneous hemorrhage ( n = 3) and 16 healthy controls was recorded while they passively listened to four pre-selected music samples by different authors (mean recording time: 3 m and 36 s ± 24 s). The parametric and non-parametric frequency spectra were computed on the heart rate, spectra were compared within/across subjects and music authors, and the spectra descriptors were entered into a 1-R rules data mining procedure (WEKA software Leave One Out and Ten Fold Cross validation). The procedure independently classified the heart rate spectral patterns of both patients and controls and the emotions reported by healthy subjects as “ positive” or “ negative”. Results In both healthy controls and vegetative state subjects, the power spectra while passively listening to music differed from baseline when compared irrespective of the music authorship and from each other when compared across music samples. Data mining sorted the nu_LF (normalized parameter unit of the spectrum low frequency range) as the significant descriptor of heart rate variability in the conditions of the study. The nu_LF classification of the healthy controls’ HRV changes in response to music replicated that based on subjective reports with 75–93.7% accuracy. Conclusions Although preliminary, these findings suggest that autonomic changes with possible emotional value can be induced by complex stimuli also in vegetative state, with implications on the residual responsiveness of these subjects. Significance Heart rate variability descriptors and data mining methods appear applicable to investigate brain function in the absence of consciousness.

Quality control and quality assurance of medical committee performance in the Israel Defense Forces

The paper aims to focus on a unique quality control system that was set up ten years ago to evaluate Medical Classification Committee decision-making processes at the Israel Defense Forces (IDF) conscription center. Two main approaches were deployed by the control system to assess medical classification committees' performance. The first was direct assessment of the medical committees' clinical work and decision-making processes. The second applied data mining procedures to the computerized medical databases. The functional classification codes (FCCs)--codes for the most common medical disorders assigned to male recruits from the central computerized central IDF database, the sub-districts comprising the recruitment centers, and the chairmen assigning the FCCs to recruits--were all analyzed. A total of 26 FCCs, each indicating a common medical problem and its severity, constituted approximately 90 percent of all FCCs assigned at recruitment centers between 2001 and 2006. Major contributors to medical profiling outcomes were underweight, asthma, chronic headache, mental illness, symptomatic scoliosis, hypnosis, chronic back pain, knee joint disorders, allergic rhinitis and sinusitis. Evaluating the computerized medical database revealed significant differences in: medical profile prevalence; recruitment center FCCs; different sub-districts in a given recruitment center; and profiling by medical committee chairmen. Findings indicate disparities between recruitment centers and their chairmen in the medical profiling process owing to variations in recruitment center working methods and medical history taking, physical examinations, interpreting medical information and individual differences in the chairmen's decision-making process. Other reasons include technician and laboratory staff inaccuracies. These significant discrepancies highlighted the need for an intervention program. To minimize variations and create a uniform work platform, an orderly instruction system and training programs for the committee chairmen, technicians and laboratory staff were established. These actions resulted in improved performance in 2007 as confirmed by a decreased variability in the assignment of medical FCCs. The paper highlights methods that can be used to assess disability screening, sports medicine committees and primary care setting procedures.

An efficient procedure for mining statistically significant frequent itemsets

We suggest the original procedure for frequent itemsets generation, which is more efficient than the appropriate procedure of the well known Apriori algorithm. The correctness of the procedure is based on a special structure called Rymon tree. For its implementation, we suggest a modified sort-merge-join algorithm. Finally, we explain how the support measure, which is used in Apriori algorithm, gives statistically significant frequent itemsets.

Basic ideas to support pressure ulcers treatment by means of image mining

A Pressure ulcer (PU) occurs in a significant amount of patients that must remain in bed without movements for long periods. To improve its treatment in order to increase patient life quality and to reduce medical healthcare is yet a challenge. It seems to be helpful to search for medical decision making information in both PU images features and in data concerning patient life so far. Once suitably stored such information sets data and image mining procedures are supposed to be able to allow inferences and finding of patient clusters. Is seems to be also possible computationally to generate a PU grade inference that will help medical experts to accomplish therapeutic procedures. Current research efforts with such purposes are here presented.

Heart deformation pattern analysis through shape modelling

In this paper, we present an approach to the description of time-varying anatomical structures. The main goal is to compactly but faithfully describe the whole heart cycle in such a way to allow for deformation pattern characterization and assessment. Using such an encoding, a reference database can be built, thus permitting similarity searches or data mining procedures.

Exploring enantioselective molecular recognition mechanisms with chemoinformatic techniques

A comprehensive review of chemoinformatic techniques and studies applied to the field of enantioselective molecular recognition is presented. Several approaches such as enantiophores/pharmacophore modelling, QSPRs, CoMFA and other insightful data mining procedures are discussed. The review focuses on the central role of chemoinformatic approaches on the establishment of connections between available experimental data, mainly HPLC separation data, and these algorithms that describe properties of chiral molecules. The general overview of the aforementioned calculations account for a use of these techniques as a valuable strategy to achieve reliable prediction systems, infer the mechanisms of chiral recognition, generate insight for the conception of new chiral receptors and corroborate and assist experimental techniques such as chiral LC. Moreover, it is pointed out that computer methods in this field promise a wide range of applications for both academia and industry, ranging from enantioselective reactions, drug discovery and analysis of high-throughput screenings, to analytical and semi-preparative separations or large-scale production of enantiopure compounds.

PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics

In recent years proteomics became increasingly important to functional genomics. Although a large amount of data is generated by high throughput large-scale techniques, a connection of these mostly heterogeneous data from different analytical platforms and of different experiments is limited. Data mining procedures and algorithms are often insufficient to extract meaningful results from large datasets and therefore limit the exploitation of the generated biological information. In our proteomic core facility, which almost exclusively focuses on 2-DE/MS-based proteomics, we developed a proteomic database custom tailored to our needs aiming at connecting MS protein identification information to 2-DE derived protein expression profiles. The tools developed should not only enable an automatic evaluation of single experiments, but also link multiple 2-DE experiments with MS-data on different levels and thereby helping to create a comprehensive network of our proteomics data. Therefore the key feature of our "PROTEOMER" database is its high cross-referencing capacity, enabling integration of a wide range of experimental data. To illustrate the workflow and utility of the system, two practical examples are provided to demonstrate that proper data cross-referencing can transform information into biological knowledge.

Personal continuous route pattern mining

In the daily life, people often repeat regular routes in certain periods. In this paper, a mining system is developed to find the continuous route patterns of personal past trips. In order to count the diversity of personal moving status, the mining system employs the adaptive GPS data recording and five data filters to guarantee the clean trips data. The mining system uses a client/server architecture to protect personal privacy and to reduce the computational load. The server conducts the main mining procedure but with insufficient information to recover real personal routes. In order to improve the scalability of sequential pattern mining, a novel pattern mining algorithm, continuous route pattern mining (CRPM), is proposed. This algorithm can tolerate the different disturbances in real routes and extract the frequent patterns. Experimental results based on nine persons’ trips show that CRPM can extract more than two times longer route patterns than the traditional route pattern mining algorithms.

Wireless SISO Channel Propagation Model for Underground Mines

Modern mining procedures aim to replace human resources directly involved in the mineral extraction process by automatic or remote control systems. Data may be collected from sensors, or data and/or video may be transmitted by telecommunication equipment. The actual technology trend is to do these transmissions using wireless technology (WSN, Wi-Fi, among others). This work summarizes the experience that has been collected from characterizing a Single-Input-Single-Output (SISO) large scale 870 MHz and 2.45 GHz wireless propagation channel in an underground copper mine. Also the Rician K factor and channel coherence time are calculated for a 2.45 GHz mobile link.

A Visual Approach to Parameter Selection of Density-Based Noise Removal for Effective Data Clustering

可视化技术的发展极大地提高了传统数据挖掘技术的效率.通过结合人类识别模式的能力,计算机程序能够更有效的发现隐藏在数据中的规律和信息.作为聚类分析的重要步骤,噪音消除一直都是困绕数据挖掘研究者的问题,尤其对于不同领域的应用,由于噪音的模型和定义不同,单一的数据处理方法无法有效而准确地去除域相关的噪音.本文针对这一问题,提出了一个新型的可视化噪音处理方法CLEAN.CLEAN的独特之处在于它设计的噪音处理技术和提出的可视化方法有机地结合在一起.噪音处理算法为可视化模型生成所需数据,同时针对噪音处理算法选择可视化方法,从而达到提高整个数据处理系统性能的目的.这样不仅降低了噪音去除过程中主观因素的影响,还可以帮助数据挖掘程序去除领域相关的噪音.同时源数据的质量,算法参数的选择和不同噪音去除算法的精确性都可以在所使用的可视化模型中反映出来.实验表明CLEAN能够有效地帮助空间数据聚类算法在噪音环境下发现数据的自然聚类.;Traditional visual data mining relies on visualization techniques to disclose implicit information and relationship among data through utilizing human capability of pattern recognition. As an important step in data clustering, noise removal is a challenging topic as domain-specific noise is not well defined and cannot be removed by generic process of data cleaning. This paper addresses two conjugated and reciprocal issues in the use of visualization in noise removal? choosing appropriate visualization techniques based on data removing methods, and designing processing algorithms that suit visualization. The goal is a synthesis of visualization techniques and data mining methods to enhance the overall performance while reducing the subjective factor in visual mining procedure. A visual data cleaning approach called CLEAN is proposed to assist spatial data clustering in four important aspects: removal of domain-specific noise, visualization of data quality, selection of algorithm parameters, and measurement of noise removing methods on parameter sensitiveness. Experiments show that the visualization models in CLEAN do assist effective discovery of natural spatial clusters in a noisy environment.

ZPLD1 gene is disrupted in a patient with balanced translocation that exhibits cerebral cavernous malformations

The past few years have seen rapid advances in our understanding of the genetics and molecular biology of cerebral cavernous malformations (CCM) with the identification of the CCM1, CCM2, and CCM3 genes. Recently, we have recruited a patient with an X/3 balanced translocation that exhibits CCM. By fluorescent in situ hybridization analysis, sequence analysis tools and database mining procedures, we refined the critical region to an interval of 200-kb and identified the interrupted ZPLD1 gene. We detected that the mRNA expression level of ZPLD1 gene is consistently decreased 2.5-fold versus control (P=0.0006) with allelic loss of gene expression suggesting that this protein may be part of the complex signaling pathway implicated in CCM formation.

Novozymes acquires Biocon's enzyme business

Easily accessible patent databases and advances in technology have enabled the exploration of organizational innovation through the analysis of patent records. However, the textual content of patents presents obstacles to gleaning useful information. In this study, we develop an expert system framework that utilizes text and data mining procedures for analyzing innovation through textual patent data. Specifically, we use patent titles representing the innovation activity at one company (SAP) and perform a bibliometric analysis using our proposed framework. Enterprise software, of which SAP is a pioneering developer, must serve a wide assortment of functions for companies in many different industries. In addition, SAP's sole focus is on enterprise software and it is a market leader in the category with substantial patent activity over the last decade. Using our framework to analyze SAP's patent activity provides a demonstration of how our bibliometric analysis can summarize and identify trends in innovation in a large software company. Our results illustrate that SAP has a breadth of innovative activity spread over the three-tier software engineering architecture and a lack of topical repetition indicative of limited depth. SAP's innovation is also seen to emphasize data management and quickly integrate emerging technologies. Results of an analysis on any company following our framework could be used for a variety of purposes, including: to examine the scope and scale of innovation of an organization, to examine the influence of technological trends on businesses, or to gain insight into corporate strategy that could be used to aid planning, investment, and purchasing decisions.

Modeling and Analysis the Web Structure Using Stochastic Timed Petri Nets

Precise analysis of the Web structure can facilitate data pre-processing and enhance the accuracy of the mining results in the procedure of Web usage mining. STPN (Stochastic Timed Petri Nets) is a high-level graphical model widely used in modeling system activities with concurrency. STPN can save the analyzed results in an incidence matrix for future follow-up analyses, and some already-verified properties held by STPN, such as reachability, can also be used to solve some unsettled problems in the model. In the present study, we put forth the use of STPN as the Web structure model. We adopt Place in the STPN model to represent webpage on the websites and use Transition to represent hyperlink. Through the model, we can conduct Web structure analysis. We simultaneously employ the Web structure analysis information in the incidence matrix and the reachability properties, obtained from the STPN model, to help proceed with pageview identification and path completion at the data preprocessing phase.