Hidden Rules Research Articles

Distribution analysis of train interval journey time employing the censored model with shifting character

ABSTRACTThe theoretical framework of limited dependent variable models is extended to accommodate a shifting character and thus fit the distribution of train journey time on sections of urban rail network. Data of actual train arrival and departure time at each station are used to calculate the journey time of each railway interval of multi-class trains. The log-normal distribution and normal distribution among a group of theoretical distributions are the most and second most suitable latent distributions of the train interval journey time in the censored models with shifting character. This modified distribution is described by four parameters, namely, the expectation and variance of the latent distribution and the upper and lower bound of the migration interval. The square root of the least square measurement (SRLSM) is taken as a measure, and a traversal search is adopted to determine the above four parameters according to the SRLSM. The average of the SRLSM of the theoretical train interval journey time distribution obtained by using the proposed method on all railway sections is 0.0905. The theoretical framework is the basis of storing hidden rules in data instead of past data of train travel time and optimizing the existing management of rail transit operation.

The Cause of the Secretary Corruption in the Perspectives of Guanxi and Hidden Rules

In recent violent anti-corruption measures of China government, the secretary corruption draws much attention. Compared to the high rank official corruption, the secretary corruption is hidden and unnoticeable. Many researchers suggest that the improvement of the morality of the secretary would curbed the secretary corruption. However, the key to curb such corruption seems not to lie in the improvement of morality, as the main cause of the corruption is not of morality. The paper set out to analyze the cause of the secretary corruption, in the perspectives of guanxi and qian guize, which provides the secretary with the informal power. Such informal power is invisible superficially, but it is effective in most of the cases. Results showed that the special guanxi between the secretary and official he or she serves provides the secretary with the informal power. With the informal power, the secretary’s rent-seeking activities cause the corruption. To curb such corruption, the erase of the secretary and official is important. In recent years, the violent anti-corruption measures of China government have curbed the corruption of both secretary and the corresponding official effectively.

Applying Data Mining Techniques to Extract Hidden Patterns about Breast Cancer Survival in an Iranian Cohort Study.

Breast cancer survival has been analyzed by many standard data mining algorithms. A group of these algorithms belonged to the decision tree category. Ability of the decision tree algorithms in terms of visualizing and formulating of hidden patterns among study variables were main reasons to apply an algorithm from the decision tree category in the current study that has not studied already. The classification and regression trees (CART) was applied to a breast cancer database contained information on 569 patients in 2007-2010. The measurement of Gini impurity used for categorical target variables was utilized. The classification error that is a function of tree size was measured by 10-fold cross-validation experiments. The performance of created model was evaluated by the criteria as accuracy, sensitivity and specificity. The CART model produced a decision tree with 17 nodes, 9 of which were associated with a set of rules. The rules were meaningful clinically. They showed in the if-then format that Stage was the most important variable for predicting breast cancer survival. The scores of accuracy, sensitivity and specificity were: 80.3%, 93.5% and 53%, respectively. The current study model as the first one created by the CART was able to extract useful hidden rules from a relatively small size dataset.

Analysis on traditional Chinese medicine prescriptions treating cancer-related anorexia syndrome based on grey system theory combined with multivariate analysis method and discovery of new prescriptions

Cancer-related anorexia syndrome (CACS) is one of the main causes for death at present as well as a syndrome seriously harming patients' quality of life, treatment effect and survival time. In current clinical researches, there are fewer reports about empirical traditional Chinese medicine(TCM) prescriptions and patent prescriptions treating CACS, and prescription rules are rarely analyzed in a systematic manner. As the hidden rules are not excavated, it is hard to have an innovative discovery and knowledge of clinical medication. In this paper, the grey screening method combined with the multivariate statistical method was used to build the ″CACS prescriptions database″. Based on the database, totally 359 prescriptions were selected, the frequency of herbs in prescription was determined, and commonly combined drugs were evolved into 4 new prescriptions for different syndromes. Prescriptions of TCM in treatment of CACS gave priority to benefiting qi for strengthening spleen, also laid emphasis on replenishing kidney essence, dispersing stagnated liver-qi and dispersing lung-qi. Moreover, interdependence and mutual promotion of yin and yang should be taken into account to reflect TCM's holism and theory for treatment based on syndrome differentiation. The grey screening method, as a valuable traditional Chinese medicine research-supporting method, can be used to subjectively and objectively analyze prescription rules; and the new prescriptions can provide reference for the clinical use of TCM for treating CACS and the drug development.

The interdisciplinary research of big data and wireless channel: A cluster-nuclei based channel model

Recently, internet stimulates the explosive progress of knowledge discovery in big volume data resource, to dig the valuable and hidden rules by computing. Simultaneously, the wireless channel measurement data reveals big volume feature, considering the massive antennas, huge bandwidth and versatile application scenarios. This article firstly presents a comprehensive survey of channel measurement and modeling research for mobile communication, especially for 5th Generation (5G) and beyond. Considering the big data research progress, then a cluster-nuclei based model is proposed, which takes advantages of both the stochastical model and deterministic model. The novel model has low complexity with the limited number of cluster-nuclei while the cluster-nuclei has the physical mapping to real propagation objects. Combining the channel properties variation principles with antenna size, frequency, mobility and scenario dug from the channel data, the proposed model can be expanded in versatile application to support future mobile research.

Hotel Staff Service Sabotage Behavior: Classification and Impact on Consumer Willingness to Pay

This paper extracts 18 staff service sabotage behavior in hotels and classifies them into five categories: “negative service attitudes”, “provide wrong service content and information”, “deliberate sabotage behavior”, “delay or not provide service” and “hidden rules”. Next, discuss their negative impact on consumer willingness to pay by separately analyzing roles of genders, ages and educational backgrounds. Results show that (1) Male consumers have higher willing to pay than female consumers and pay more attention on service efficiency. (2) The consumer willingness to pay is negative related with ages and consumers who aged between 25 and 50 focus more in service attitudes. (3) High educational background means low willingness to pay. Interestingly, consumers who educated most rank “hidden rules” as the first, while others rank it as the last.

Anticancer Drug Combinations, A Big Momentum is Needed

Most cancer therapies are seldom effective by using one anticancer drug owing to refractory and resistance property of cancer tissues caused by multiple genetic alterations and molecular abnormalities. Despite great popularity of anticancer drug combination utilizations, the hidden rules behind scenarios emerge in new era of anticancer therapy. Most importantly, anticancer drug cocktails need to transform from empirical to science-guided enterprises. This editorial offers the background knowledge of drug combination therapy across the history. Possible future landscapes and drawbacks of current cancer drug combinative therapy are addressed and speculated.

How to become a successful researcher: tips for early career researchers.

How to become a successful researcher: tips for early career researchers.

프로세스 마이닝 기법을 활용한 고장 수리 프로세스 분석

최근 기업의 비즈니스 프로세스를 혁신하고 효율화하기 위한 다양한 연구가 활발히 진행되고 있는 가운데, 의미 있는 비즈니스 프로세스 모델을 생성하고 분석하는 프로세스 마이닝 연구 분야가 주목받고 있다. ERP (Enterprise Resource Planning) 시스템이나 BPM (Business Process Management) 시스템에서 발생되는 업무 처리 내역이나 이벤트 로그를 분석하여 의미 있는 정보나 규칙을 발견해 낼 수 있는 프로세스 마이닝 기법은 다양한 분야에서 적용되고 있다. 본 연구에서는 프로세스 마이닝 도구인 ProM 시스템을 실제 고장 수리 사례에 적용하여 고장 수리 프로세스를 분석하고 제품의 주요 고장 패턴을 발견하는 방법을 제시한다. 고장 수리 프로세스 분석 결과 단순 통계 분석 결과에서 발견할 수 없었던 연결된 흐름의 빈도 분석이 가능하였으며, 연결된 흐름들 중에서 문제가 되는 프로세스에 대한 업무 향상 방안을 제시할 수 있었다. Recently, studies about process mining for creating and analyzing business process models from log data have received much attention from BPM (Business Process Management) researchers. Process mining is a kind of method that extracts meaningful information and hidden rules from the event log of enterprise information systems such as ERP and BPM. In this paper, repair processes of electronic devices are analyzed using ProM which is a process mining tool. And based on the analysis of repair processes, the method for finding major failure patterns is proposed by multi-dimensional data analysis beyond simple statistics. By using the proposed method, the reliability of electronic device can be increased by providing the identified failure patterns to design team.

Hemodialysis Key Features Mining and Patients Clustering Technologies

The kidneys are very vital organs. Failing kidneys lose their ability to filter out waste products, resulting in kidney disease. To extend or save the lives of patients with impaired kidney function, kidney replacement is typically utilized, such as hemodialysis. This work uses an entropy function to identify key features related to hemodialysis. By identifying these key features, one can determine whether a patient requires hemodialysis. This work uses these key features as dimensions in cluster analysis. The key features can effectively determine whether a patient requires hemodialysis. The proposed data mining scheme finds association rules of each cluster. Hidden rules for causing any kidney disease can therefore be identified. The contributions and key points of this paper are as follows. (1) This paper finds some key features that can be used to predict the patient who may has high probability to perform hemodialysis. (2) The proposed scheme applies k-means clustering algorithm with the key features to category the patients. (3) A data mining technique is used to find the association rules from each cluster. (4) The mined rules can be used to determine whether a patient requires hemodialysis.

KNOWLEDGE DISCOVERY FROM DATA SETS THROUGH DATA MINING TECHNIQUES: APPLICATION TO MEDICAL DATA MINING

A rapid development and growing use of database systems, and the importance of information stored in these systems raise the issue of how to make the best use of these systems. One of the leading area where the database systems are mostly used is 'medicine'. Today, all patients' laboratory results, patient history as well as x-ray images and more are kept in databases. These of the traditional database query and report methods to filter information and to present reports does not always provide the important hidden rules contained in the stored data. Therefore, the use of data mining techniques used for knowledge discovery in this area from databases is inevitable. In this study, the rules base is created thorough the knowledge discovery by employing REX-1 algorithm, a data mining technique, on the Wisconsin Breast Cancer, Ljubljana Breast Cancer, Dermatology, Hepatitis and Diabetes sample sets, which are real life data and commonly used in the medical field. In terms of the accuracy rate, the results of this study were compared to the results of the algorithms widely used in this field, such as C4.5, NavieBayes, PART, CN2, CORE, GA-SVM.

ANALYSIS OF MESENCHYMAL STEM CELL DIFFERENTIATION IN VITRO USING CLASSIFICATION ASSOCIATION RULE MINING

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

On governance in the long-term vegetation process: How to discover the rules?

Ecological practice is telling us that to identify Nature’s rules, we should look for regularities in the resulting effects. Hidden rules are involved and the effects are manifested by compositional, functional, and structural transitions. This paper’s focus is on two conjectures regarding the governance of specific transition components, the first supposedly under global co-ordination, and the second under superimposed site specific instability oscillations. The reality of any apparent regularity in these is the sole condition for the regularity’s acceptance as a rule. Reality is testable but in retrospect only, based on time series analyses. Since long pollen spectra supply the evidence, the time period involved is measured in thousands of years. For maximal usefulness, a spectrum should have a long period length, dated horizons intensely sampled at short time steps, and precisely identified taxa. Period length and time step width matter because both may have a masking effect on the regularities. There is, of course, a natural limit for period length, which is set by the age of the pollen bearing sediments. We completed the analysis of 23 spectra using techniques deemed suitable for testing the conjectures. The spectra originated from sites in the Americas where we found suitable spectra in sufficient numbers and in geographic contiguity from the Arctic region to the Antarctic. The presented results have clear indications that the two conjectures identify real rules. The main body of the paper narrates the analyses and provides explanations. Informative materials, too voluminous for inclusion in the paper, are made available on the Internet at URL: www.vegetationdynamics.com linking to “Appendices Ta”.

Unveiling the underlying relationships over a network for monitoring purposes

AbstractNowadays traffic monitoring and analysis tools provide poor information about traffic volume without giving any clear view of what the hidden rules and relationships that govern these flows are. Since the majority of flows is generated by services (web browsing, email, p2p) and most of these applications are dependent on many network assets (servers and databases) we should discover the underlying relationships of every application. We present a technique that discovers the hidden relationships among components of a network that consist of parts of specific applications. From time information and flow attributes, such as IP addresses and service ports, our method using a novel hybrid genetic algorithm produces a small set of fuzzy rules that can reveal the underlying relationships over a network without any guidance. These dependencies build a service graph which can become a useful tool for fault localization, monitoring service performance, designing changes and anomaly detection. Copyright © 2009 John Wiley & Sons, Ltd.

From Passivity to Potentiality: The <em>Communitas</em> of Stillness

From Passivity to Potentiality: The <em>Communitas</em> of Stillness

KISTCM: knowledge discovery system for traditional Chinese medicine

Objective: Traditional Chinese Medicine (TCM) provides an alternative method for achieving and maintaining good health. Due to the increasing prevalence of TCM and the large volume of TCM data accumulated though thousands of years, there is an urgent need to efficiently and effectively explore this information and its hidden rules with knowledge discovery in database (KDD) techniques. This paper describes the design and development of a knowledge discovery system for TCM as well as the newly proposed KDD techniques integrated in this system. Methods: A novel Knowledge dIscovery System for TCM (KISTCM) is developed by incorporating several data mining techniques, primarily including a medicine dependency relationship discovery algorithm, an efficacy dimension reduction algorithm based on neural networks, a method for exploring the relationships between formulae and syndromes using gene expression programming (GEP), and an approach for discovering the properties in terms of nature, taste and meridian based on the herbal dosage by employing the effect degree function to calculate the effect of each property. Results: Representative experimental cases are used to evaluate the system performance. Encouraging results are obtained, including rules previously unknown to algorithm designers and experiment runners. Experiments demonstrate that KISTCM has powerful knowledge discovery and data analysis capabilities, and is a useful tool for discovering the underlying rules in formulae. Our proposed techniques successfully discover hidden knowledge from TCM data, which is a new direction in knowledge discovery. From TCM experts' perspective, the accuracy of data analysis for KISTCM is an improvement, and these results compare favorably to other existing TCM data mining techniques. The system could be expected to be useful in the practice of TCM, e.g., assisting TCM physicians in prescribing formulae or automatically distinguishing between minister and assistant herbs in a formula.

A New Approach to Sensitive Rule Hiding

Privacy preserving data mining is a novel research direction in data mining and statistical databases, which has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. There have been two types of privacy proposed concerning data mining. The first type of privacy, called output privacy, is that the data is altered so that the mining result will preserve certain privacy. The second type of privacy, called input privacy, is that the data is manipulated so that the mining result is not affected or minimally affected. For output privacy in hiding association rules, current approaches require hidden rules or patterns to be given in advance. However, to specify hidden rules, entire data mining process needs to be executed. For some applications, only certain sensitive rules that contain sensitive items are required to hide. In this work, an algorithm SRH (Sensitive Rule Hiding) is proposed, to hide the sensitive rules that contain sensitive items, so that sensitive rules containing specified sensitive items on the right hand side of the rule cannot be inferred through association rule mining. Example illustrating the proposed approach is given. The characteristics of the algorithm are discussed.

Book Review: - Watching the English: The Hidden Rules of English Behaviour

Book Review: - Watching the English: The Hidden Rules of English Behaviour

Diabetes and Thyroid Disease: Nursing Care to Improve Outcomes for Patients Living in Poverty

A primary goal of nurses providing care for persons who have diabetes mellitus or thyroid disease is improving their health outcomes. For persons who have diabetes and thyroid disease and live in poverty, improving the care process, and ultimately health outcomes, must include the nurse's understanding of poverty. The purpose of this article is to provide nurses with a basic understanding of the resource issues, "hidden rules," and characteristics that are associated with persons who live in poverty. Most importantly, some basic strategies to improve the health outcomes of patients who have diabetes or thyroid disease and live in poverty are provided.

Jammed in an Unknown Device

Jammed in an Unknown Device