Multivariate discretization of continuous valued attributes.

The process of extracting meaningful rules from big and complex data is called data mining. Data mining has an increasing popularity in every field today. Data units are established in customer-oriented industries such as marketing, finance and telecommunication to work on the customer churn and acquisition, in particular. Among the data mining methods, classification algorithms are used in studies conducted for customer acquisition to predict the potential customers of the company in question in the related industry. In this study, bank marketing data set in UCI Machine Learning Data Set was used by creating models with the same classification algorithms in different data mining programs. Accuracy, precision and f- measure criteria were used to test performances of the classification models. When creating the classification models, the test and training data sets were randomly divided by the holdout method to evaluate the performance of the data set. The data set was divided into training and test data sets with the 60-40%, 75­25% and 80-20% separation ratios. Data mining programs used for these processes are the R, Knime, RapidMiner and WEKA. And, classification algorithms commonly used in these platforms are the k-nearest neighbor (k-nn), Naive Bayes, and C4.5 decision tree.

This focus issue of Physiological Measurement follows the 38th Annual International Computing in Cardiology (CinC) Conference, hosted in Hangzhou, China in September 2011 by Zhejiang University. Each year, the NIH-sponsored PhysioNet resource (http://physionet.org/) runs an open competition lasting several months, aimed at encouraging the development of solutions to an unsolved or poorly solved problem in biomedicine, in most cases making use of relevant clinical and experimental data provided freely by PhysioNet. Participants in these annual challenges discuss their diverse approaches to the Challenge problems during dedicated scientific sessions at CinC. The topics of these PhysioNet/CinC Challenges range from physiologic signal processing and analysis to forecasting and modelling clinically important events and processes.

While there exist many efficient algorithms in the literature for learning Bayesian networks with discrete random variables, learning when some variables are discrete and others are continuous is still an issue. A common way to tackle this problem is to preprocess datasets by first discretizing continuous variables and, then, resorting to classical discrete variable-based learning algorithms. However, such a method is inefficient because the conditional dependences/arcs learnt during the learning phase bring valuable information that cannot be exploited by the discretization algorithm, thereby preventing it to be fully effective In this paper, we advocate to discretize while learning and we propose a new multivariate discretization algorithm that takes into account all the conditional dependences/arcs learnt so far. Unlike popular discretization methods, ours does not rely on entropy but on clustering using an EM scheme based on a Gaussian mixture model. Experiments show that our method significantly outperforms the state-of-the-art algorithms.

Decision tree is one of the most widely used and practical methods in the data mining and machine learning discipline. However, many discretization algorithms developed in this field focus on univariate only, which is inadequate to handle the critical problems especially owned by medical domain. In this paper, we propose a new multivariate discretization method called multivariate interdependent discretization for continuous attributes - MIDCA. Our novel algorithm can minimize the uncertainty between the interdependent attribute and the continuous-valued attribute, and at the same time to maximize their correlation. The empirical results demonstrate a comparison of performance of various decision tree algorithms on twelve real-life datasets from UCI repository.

Modern information technology makes it possible today to collect, store, transfer, and combine huge amounts of data at very low costs. Thus more and more companies, and scientific and governmental institutions build up large archives of all kinds of data like numbers, tables, documents, images, sounds, etc. Although users often have a vague understanding of their data and can usually formulate hypotheses and guess dependencies, turning these – often abundantly available – data into useful information turns out to be rather difficult. In response to these challenges a new area of research has emerged, called ‘‘knowledge discovery in databases’’ or ‘‘data mining’’, that tries to provide tools to extract valid, useful, understandable, unknown, and unexpected relationships from large databases [1]. This current form of data analysis is an interdisciplinary field, and employs methods from statistics, soft computing, artificial intelligence and machine learning. The stress lies on the development of techniques that produce human-understandable results, and are suited for large, real world datasets. The data in real world applications has in most cases characteristics that challenge classical analyzing approaches. Besides being heterogeneous – which in its simplest form can mean that we have to deal with numerical and symbolic features – , the data is often of low quality. Algorithms must thus be able to deal with uncertainty and imprecision. The characteristics of the data sources – their quantity, complexity, dimensionality and imperfection – , the essential of extracting understandable patterns from these, and the need to incorporate available background knowledge in that process, makes us assume that (neuro-) fuzzy techniques will play a considerable role in the future of data mining [3]. It is the ability of fuzzy sets to transform between computer representations and (naturally linguistic) human concepts that makes them so valuable to meet the advanced data mining demands, and that Zadeh meant, when he promoted his idea of computing with words [4]. The inherent imprecision of words is not necessarily a weakness, but, on the contrary, can be crucial to model complex systems. From our own experience we observed that many practical applications have this certain robustness where full precision is not necessary. In such cases, exaggerated precision can be a waste of resources, and solutions obtained using fuzzy approaches might be easier to understand and to apply, and gain their strengths by explicitly taking into account vagueness, imprecision or uncertainty. One prominent way to use fuzzy systems in data analysis, is to induce fuzzy if-then rules from data by neurofuzzy techniques. The use of linguistic variables eases the readability and interpretability of the rule base. If we apply such techniques, we must be aware of the trade-off between precision and interpretability. However, the results in data mining are not only judged for their accuracy, but also for their interpretability, as the ultimate goal is to extract human understandable patterns [2]. The papers in this special issue of the Soft Computing Journal deal with quite different aspects of neuro-fuzzy techniques, with a stress on data analysis and rule based systems. Although the intention of this issue cannot be to give a survey of this wide area, it shall give an insight in current trends and research topics in this field. The papers can roughly be divided into three groups:

Intelligent Data Analysis (IDA) approach proves its ability to deal with small and huge data sets; this ability can be done by generating and developing new methodologies. While; Knowledge Discovery in Database (KDD) aiming to automatic interpretation of large data sets. As a result, we can conclude a strong relationship between two concepts. This study discusses five challenges related to data analytical to making right decision. These challenges include: Missing values; Data scarcity, Data dimensionality reduction, Black box; and Mathematical model. First: One of the important trends in KDD will be the growing importance of data processing. But this point faces problems similar to those of data mining (High dimensional data, missing values imputation and data integration). As explained before, one of the open still problems in estimation missing values methods are how to select the optimal number of nearest neighbors of those values. Second: Data scarcity (insufficient size of dataset) problem is one of the challenges in KDD. Insufficient size of data is very often responsible for the poor performances in learning. Third: The goal of dimension reduction methods is using the correlation structure among the predicator variables to reduction the three main dimensions (features, samples and value of features). But, how we can combination among these three dimensions without lose any important inform is still as one of the challenges in KDD system. Fourth: As we know data mining algorithm are “black box character” for many reasons explained in chapter two. But how we can convert it to system will behaviors and conclusion that can be explained and analyzed in a comprehensible manner (i.e., white box) is remained one of the main challenges of KDD system.

IFN-signaling gene (ISG) expression scores are potential markers of inflammation with significance from cancer to genetic syndromes. In Aicardi Goutières Syndrome (AGS), a disorder of abnormal DNA and RNA metabolism, this score has potential as a diagnostic biomarker, although the approach to ISG calculation has not been standardized or validated. To optimize ISG calculation and validate ISG as a diagnostic biomarker, mRNA levels of 36 type I IFN response genes were quantified from 997 samples (including 334 AGS), and samples were randomized into training and test data sets. An independent validation cohort (n = 122) was also collected. ISGs were calculated using all potential combinations up to 6 genes. A 4-gene approach (IFI44L, IFI27, USP18, IFI6) was the best-performing model (AUC of 0.8872 [training data set], 0.9245 [test data set]). The majority of top-performing gene combinations included IFI44L. Performance of IFI44L alone was 0.8762 (training data set) and 0.9580 (test data set) by AUC. The top approaches were able to discriminate individuals with genetic interferonopathy from control samples. This study validates the context of use for the ISG score as a diagnostic biomarker and underscores the importance of IFI44L in diagnosis of genetic interferonopathies.

Data mining and knowledge discovery aim at finding useful information from typically massive collections of data, and then extracting useful knowledge from the information. To date a large number of approaches have been proposed to find useful information and discover useful knowledge; for example, decision trees, Bayesian belief networks, evidence theory, rough set theory, fuzzy set theory, kNN (k-nearest-neighborhood) classifier, neural networks, and support vector machines. However, these approaches are based on a specific data type. In the real world, an intelligent system often encounters mixed data types, incomplete information (missing values), and imprecise information (fuzzy conditions). In the UCI (University of California – Irvine) Machine Learning Repository, it can be seen that there are many real world data sets with missing values and mixed data types. It is a challenge to enable machine learning or data mining approaches to deal with mixed data types (Ching, 1995; Coppock, 2003) because there are difficulties in finding a measure of similarity between objects with mixed data type attributes. The problem with mixed data types is a long-standing issue faced in data mining. The emerging techniques targeted at this issue can be classified into three classes as follows: (1) Symbolic data mining approaches plus different discretizers (e.g., Dougherty et al., 1995; Wu, 1996; Kurgan et al., 2004; Diday, 2004; Darmont et al., 2006; Wu et al., 2007) for transformation from continuous data to symbolic data; (2) Numerical data mining approaches plus transformation from symbolic data to numerical data (e.g.,, Kasabov, 2003; Darmont et al., 2006; Hadzic et al., 2007); (3) Hybrid of symbolic data mining approaches and numerical data mining approaches (e.g.,, Tung, 2002; Kasabov, 2003; Leng et al., 2005; Wu et al., 2006). Since hybrid approaches have the potential to exploit the advantages from both symbolic data mining and numerical data mining approaches, this chapter, after discassing the merits and shortcomings of current approaches, focuses on applying Self-Organizing Computing Network Model to construct a hybrid system to solve the problems of knowledge discovery from databases with a diversity of data types. Future trends for data mining on mixed type data are then discussed. Finally a conclusion is presented.

The role of pertinently diversified and balanced training as well as testing data sets in achieving the true performance of classifiers in predicting the antifreeze proteins

Application of Chi-square discretization algorithms to ensemble classification methods

The effects of data preprocessing in the determination of coagulant dosing rate

The aim of this study is to present a method of assessing eight types of mood that is optimized to every individual on the basis of the heart rate variability (HRV) data which, to eliminate the influence of the inter-individual variability, are measured in a long time period during daily life. Eight types of mood are happiness, tension, fatigue, anxiety, depression, anger, vigor, and confusion. HRV and body accelerations were recorded from nine normal subjects for two months of normal daily life. Fourteen HRV indices were calculated with the HRV data at 512 seconds prior to the time of every mood level report. Data to be analyzed were limited to those with body accelerations of 30 mG (0.294 m/s2) and lower. Further, the differences from the reference values in the same time zone were calculated with both the mood score (Δmood) and HRV index values (ΔHRVI). The multiple linear regression model that estimates Δmood from the scores for principal components of ΔHRVI were then constructed for each individual. The data were divided into training data set and test data set in accordance with the 2-fold cross validation method. Multiple linear regression coefficients were determined using the training data set, and with the optimized model its generalization capability was checked using the test data set. The model was most effective on estimating tension compared with other seven types of mood. The subjects' mean Pearson correlation coefficient was 0.52 with the training data set and 0.40 with the test data set. We proposed a method of assessing mood that is optimized to every individual based on HRV data measured over a long period of daily life.

Rapid Detection of Clostridium difficile Toxins in Stool by Raman Spectroscopy

The aim of this study is to highlight the importance of the unsupervised learning in Data mining and CRM fields. Data mining commonly known by its acronym KDD: knowledge discovery in data base, it refers to all methods and algorithms used for data exploration or prediction in large data bases volumes, Data mining is very important in various fields such as science, business and other areas deal with a large data set. CRM: Customer Relationship Management is an integrated information system that is used to plan, schedule and control the pre-sales and post-sales activities in an organization, both CRM and data mining techniques helps organizations maximize the value of every customer interaction and drive superior corporate performance. Clustering is one of the favoured used methods in data mining: The objective of this study is to implement the clustering algorithm K-Medoids via a shell script applied on a set of Analytical CRM data stored in Teradata environment.

The thriving medical applications of data mining in the fields of medicine and public health has led to the popularity of its use in knowledge discovery in databases (KDD). Data mining has revealed novel biomedical and healthcare acquaintances for clinical decision making that has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Disease diagnosis is one of the applications where data mining tools are establishing the successful results. Data mining intends to endow with a systematic survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research. Discussion is made to enable the disease diagnosis and the breakthrough of hidden healthcare patterns from related databases is offered. Also, the use of data mining to discover such relationships as those between health conditions and a disease is presented. It further discusses about the tools that can be used for the processing and classification of data. This paper summarizes various technical articles on medical diagnosis and prognosis. It has also been focused on current research being carried out using the data mining techniques to enhance the disease(s) forecasting process. This research paper provides future trends of current techniques of KDD, using data mining tools for healthcare. It also confers significant issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and preventable hospital deaths. The root causes of all diseases get closer towards drugs i.e. the foremost risk factor of all hilarious diseases. Drug addiction using WEKA has been used that brings into light concerning majority of drug abusers started abusing drugs at age below 20yrs.It is to make aware the druggist about the various diseases that are caused with heavy or long term intake of drugs in their life. So, to make an expert system that will awake the youth about precarious use of drugs and also alert the affected person.