Correlation Rules Research Articles

Prediction of Users Behavior through Correlation Rules

Web usage mining is an application of Web mining which focus on the extraction of useful information from usage data of severs logs. In order to improve the usability of a Web site so that users can more easily find and retrieve information they are looking for, we proposed a recommendation methodology based on correlation rules. A correlation rule is measured not only by its support and confidence but also by the correlation between itemsets. Proposed methodology recommends interesting Web pages to the users on the basis of their behavior discovered from web log data. Association rules are generated using FP growth approach and we used two criteria for selecting interesting rules: Confidence and Cosine measure. We also proposed an algorithm for the recommendation process.

A new logic correlation rule for HIV-1 protease mutation

Mining association rule in a large database is a technique for finding relations among attributes. In the last decade, most studies have been devoted to boost the efficiency, but few of them have been concentrated on the analysis of logic correlation among variables. Furthermore, mining association rules in a large database, when applied on a bio-sequence data set, is generally medically irrelevant and difficult to analyze. In this paper, a pre and post-processing approach through discovering a logic correlation rule by combining Apriori-based method and Boolean function simplification technique called Apriori_BFS method is presented. The objective of the proposed method is to effectively reduce the number of rules and present an integration logic correlation rule to readers. The experiment was conducted by using a real-world case, the HIV Drug Resistance Database, and its results unveil that the proposed method, Apriori_BFS, can not only present the logic correlation among variables but also provide more condensed rules than the Apriori method alone.

Efficient algorithms for the mining of constrained frequent patterns from uncertain data

Mining of frequent patterns is one of the popular knowledge discovery and data mining (KDD) tasks. It also plays an essential role in the mining of many other patterns such as correlation, sequences, and association rules. Hence, it has been the subject of numerous studies since its introduction. Most of these studies find all the frequent patterns from collection of precise data, in which the items within each datum or transaction are definitely known and precise. However, there are many real-life situations in which the user is interested in only some tiny portions of these frequent patterns. Finding all frequent patterns would then be redundant and waste lots of computation. This calls for constrained mining , which aims to find only those frequent patterns that are interesting to the user. Moreover, there are also many real-life situations in which the data are uncertain. This calls for uncertain data mining . In this article, we propose algorithms to efficiently find constrained frequent patterns from collections of uncertain data .

Unifying perceptual and behavioral learning with a correlative subspace learning rule

For an animal to survive it has to excel in a twofold task: It has to perceive the world and execute adequate actions. These skills are acquired and adapted through perceptual and behavioral learning, respectively. Perceptual and behavioral learning are tightly interwoven, choosing the adequate action is only possible in the presence off accurate perceptions. Learning to perceive accurately does, however, happen while acting in the world. The nature of this interaction is not well understood as theoretical work does mostly investigate the two forms of learning separately. To overcome this limitation we combine perceptual and behavioral learning in a subspace learning algorithm. In a formal analysis and in numerical simulations we show that the proposed subspace learning algorithm allows us to integrate both learning systems and to smoothly change form perceptual learning only to behavioral learning only. Further we show that in a robot open area foraging task an active adaptation of the balance between perceptual and behavioral learning is necessary in order to stabilize the performance of the robot. This alludes to a fundamental argument for the necessity of a task dependent modulation of perceptual and behavioral learning as found in biological systems.

Frame synchronization based on multiple frame observations

We consider frame synchronization for fixed frame length communication system with periodically embedded sync words. In the past few decades, numerous researchers have made significant contributions in various aspects. However, it is almost surprising that a scrutinization over the literature reveals to us an important common assumption: frame synchronization decision-making through single-frame observation. Intuition tells us that an enlarged multiple-frame observation set bears the potential to reduce sync word identification ambiguities and also to combat multi-path fading through ¿frame diversity¿. The objective of this work is thus to characterize the performance improvement of using multiple-frame decision rules rather than the single-frame decision rules considered in the previous work. We select the work of Lui and Tan as our standing point, and have conducted a comparative study in a very comprehensive manner. In particular, we shall show that the benefits of multipleframe based decision rules can be substantial. For example, in many cases, the performance of the correlation rule based on double-frame observation is superior to that of the optimum maximum likelihood rule with single-frame observation. While further enlarging the observation period beyond double frames was found to yield diminishing returns.

Systematic Study of the Multiple-Element Filling in Caged Skutterudite CoSb3

Impurity filling fraction limits (FFLs) in nearly 20 dual-element-filled CoSb3 are investigated via ab initio density functional calculations. The two types of filling impurities are selected from rare-earths (La, Ce, Eu, Yb), alkaline-earths (Ba, Sr), and alkaline metals (Na, K) with different chemical nature. Our results show that alkaline-earth and alkaline metal atoms can be jointly filled into the crystal voids, and the total filling fraction limits follow a linear correlation rule, that is, the filling fraction of one element decreases linearly whereas the other element’s increases. Cofilling of alkaline-earth (or alkaline metal) with rare-earth shows one filler dominant, usually alkaline-earth (or alkaline metal), or the one with relatively high filling fraction limit. Rare-earth impurities as cofilling atoms have relatively lower filling fractions than alkaline-earth (or alkaline metal) atoms. Eu has a relatively high FFL comparable to the FFLs of alkaline-earths, and the total FFL for the Na−Eu cofilled CoSb3 may be higher than that of the Na-single-filled CoSb3 (65%, the highest FFL). A model to estimate the FFLs for the filling with more than two types of elements in filled skutterudites is also discussed.

Investigation of selective molecular interactions using two-dimensional Fourier transform IR spectroscopy

A novel method of studying molecular interactions is introduced. It is a method based on the framework of a two-dimensional (2D) infrared (IR) correlation spectroscopy technique with a new data pretreatment strategy. In this method, an additional external perturbation stimulates the system to cause some selective changes in the state, order, and surroundings of system constituents. The overall response of the stimulated system to the applied external perturbation leads to distinctive changes in the measured spectrum, and a series of perturbation-induced dynamic spectra are collected in a systematic manner. Such a set of dynamic spectra are then transformed into a set of 2D correlation spectra by cross-correlation analysis. Temperature was chosen as an external perturbation, and the molecular interaction between 4-aminopyridine (Apy) and methacrylic acid (MAA) was investigated by 2D IR correlation spectroscopy. Synchronous cross peaks exist between the stretching vibration of the C-O group of MAA at 1,298 and 1,202 cm(-1) and the C=N group of Apy at 1,531 cm(-1), and between the carbonyl group of MAA at 1,705 cm(-1) and the amino group of Apy at 3,382 and 3,212 cm(-1). The synchronous cross peaks are from orientation of MAA and Apy vibrations generated at the same time; the synchronization of microstructure movements in the molecules indicates that there exists strong interactions between MAA and Apy. According to 2D correlation rules, static electricity and hydrogen-bonding interactions exist between Apy and MAA. Such results were further verified by (1)H-NMR spectroscopy. The successful application demonstrates that 2D IR correlation spectroscopy may be a convenient and effective method in the study of molecular interactions.

THE UNBEARABLE LIGHTNESS OF INTERNATIONAL OBLIGATIONS: WHEN AND HOW TO EXERCISE JURISDICTION UNDER CANADA’S CRIMES AGAINST HUMANITY AND WAR CRIMES ACT

This study offers an analysis of “when” and “how” Canada may –or must– exercise jurisdiction over suspected perpetrators of genocide, crimes against humanity and war crimes. The first part looks at the legislative choices made in the Crimes against Humanity and War Crimes Act as to temporal, territorial and extraterritorial jurisdiction, including universal jurisdiction and the requirement of the presence of the accused on Canada’s territory, in light of the correlative rules and obligations of international law. The second part of the study is concerned with “how” Canada will –or should– decide to exercise jurisdiction. It describes the political safeguards put in place by the Act and assesses the criteria that guide –or should guide– the exercise of prosecutorial discretion. The “when” and the “how” are clearly intertwined. Indeed, the criteria upon which the Attorney General should base his or her decision to prosecute a person cannot– or should not– be dissociated from Canada’s international obligations and responsibilities in this regard. This study examines Canada’s approach regarding suspected war criminals present on its territory, which combines criminal and administrative remedies. It assesses some of the challenges it faces in living up to its commitment to fight impunity for the worst international crimes, in light of the “unbearable lightness” of international obligations.

Logical inventory database integration into alarm correlations discovery environment

Network elements from telecommunications network have capability to generate unsolicited messages known as notifications. In the case of network resource malfunctioning, notification, called alarm, is carrying details about malfunction. When general network problem occurs, it is represented as a sequence of alarms coming from one or more different network elements. Typically, alarms are processed by network operators, requiring response action within reasonable time interval. If network operator is overloaded with huge number of alarms, time needed for network problem recognition may increase. The worst case scenario – problem will be detected after occupation of call centre by customer calls. Hence, it is necessary to recognise critical network problems automatically, reducing and correlating incoming alarms. This paper describes architecture of alarm basic correlation discovery environment (ABCDE). It is aimed for correlation rules discovery for both types of correlations described in paper: low-level ('smart' filtrations) and high-level (recognition of typical alarm sequences) correlations. Potential usage of mathematical Apriori algorithm is presented, together with integration of logical inventory database, used for including network structure knowledge in correlation process. Finally, some experimental results are presented.

Exploiting association and correlation rules parameters for learning Bayesian networks

In data mining, association and correlation rules are inferred from data in order to highlight statistical dependencies among attributes. The metrics defined for evaluating these rules can be exploited to score relationships between attributes in Bayesian network learning. In this paper, we propose two novel methods for learning Bayesian networks from data that are based on the K2 learning algorithm and that improve it by exploiting parameters normally defined for association and correlation rules. In particular, we propose the algorithms K2-Lift and K2-$X^{2}$, that exploit the lift metric and the $X^2$ metric respectively. We compare K2-Lift, K2-$X^{2}$ with K2 on artificial data and on three test Bayesian networks. The experiments show that both our algorithms improve K2 with respect to the quality of the learned network. Moreover, a comparison of K2-Lift and K2-$X^{2}$ with a genetic algorithm approach on two benchmark networks show superior results on one network and comparable results on the other.

Event Correlation in Heterogeneous EnvironmentsEreigniskorrelation in heterogenen Umgebungen

Abstract Complex event processing (CEP) is an emerging software technology for detecting business-relevant situations in streams of events and for providing these detected situations to business processes that are interested in it. While currently CEP-systems are mostly deployed within a single business domain at a limited scale, the cooperative nature of business applications gives reason that complex event processing will soon address multiple business domains and involve an increasingly large number of business events. In order to ensure interoperability as well as efficient utilization of processing and network capability, we motivate the need for heterogeneous correlation technology in the context of business applications. In this article we give an overview of the project “Distributed heterogeneous event processing” (DHEP) involving the IBM Böblingen lab and the Universität Stuttgart, where this need is adressed. The key aspect of the project is the deployment of collections of event correlation rules to a network of heterogeneous event correlation engines. We give an overview of challenges and possible solutions for the dynamic configuration of such environments and present our architecture which supports network-wide cooperation between different correlation engines.

Group integration, projected basis functions, and correlation rules for linear molecules

The technique of group integration is developed for the mixed discrete-continuous groups C∞v and D∞h, and is used to derive projected basis functions and correlation rules for linear molecules. The Hund-Wigner-Witmer correlation rules for diatomic states are shown to be special cases of the more general rules for linear molecules.

Method for Chinese short text classification based on feature extension

In this paper,based on the characteristics that short texts describe weak signals,a method based on feature extension(STCFE) was introduced to classify Chinese short texts.In this method,the correlation rules between feature items of training set and testing set were mined by FP-Growth algorithm,and then those rules were applied to extend the features of the testing set.Meanwhile,to classify Chinese short texts effectively,semantic information was introduced and the DEF term formula of words was improved in HowNet.Experimental results show that the proposed method performs well,and its Micro-F1 and Macro-F1 are higher than those of conventional approaches.

A Logical Approach to Home Healthcare with Intelligent Sensor-Network Support

This paper describes an intelligent home healthcare system characterized by a wireless sensor network (WSN) and a reasoning component. The aim of the system is to allow constant and unobtrusive monitoring of a patient in order to enhance autonomy and increase quality of life. Data collected by the sensor network are used to support a reasoning component, which is based on answer set programming (ASP), in performing three main reasoning tasks: (i) continuous contextualization of the physical, mental and social state of a patient, (ii) prediction of possibly risky situations and (iii) identification of plausible causes for the worsening of a patient's health. Starting from different data sources (sensor data, test results, inference results) the reasoning component applies expressive logic rules aimed at correct interpretation of incomplete or inconsistent contextual information, and evaluates correlation rules expressed by clinicians. The expressive power of ASP allows efficient enough reasoning to support prevention, while declarativity simplifies rule-specification and allows automatic encoding of knowledge. Preliminary evaluations show that the combination of an ASP-based reasoning component and a WSN is a good solution for creating a home-based healthcare system.

Controlling a system with redundant degrees of freedom. I. Torque distribution in still standing stick insects

The question is investigated as to how a stick insect solves the task of distributing its body weight onto its six legs, i.e., how are the torques coordinated that are produced by the 18 joints (3 per leg). Three-dimensional force measurements of ground reaction forces have been used to calculate the torques developed by each of the 18 joints. Torques were found to change considerably although the body and the legs of the animal did not move. This result implies a tight cooperation between the 18 joint controllers. Indeed, in each individual experiment, strong correlations could be observed between specific pairs of joints. However, in spite of thorough analysis, no general correlation rules between torques could be detected. The only common attribute found for all experiments was that high absolute torques observed at the beginning of the experiment tend to converge to some minimum over time. Thus, the insects tend to decrease the torques while standing still, but do not use fixed rules. Rather they appear to exploit their extra degrees of freedom and produce time courses that can strongly vary between experiments. Possible mechanisms underlying this behaviour are discussed in a companion paper [Lévy and Cruse (2008) Controlling a system with redundant degrees of freedom: ii. solution of the force distribution problem without a body model, submitted].

Microbial genotype–phenotype mapping by class association rule mining

Motivation: Microbial phenotypes are typically due to the concerted action of multiple gene functions, yet the presence of each gene may have only a weak correlation with the observed phenotype. Hence, it may be more appropriate to examine co-occurrence between sets of genes and a phenotype (multiple-to-one) instead of pairwise relations between a single gene and the phenotype. Here, we propose an efficient class association rule mining algorithm, netCAR, in order to extract sets of COGs (clusters of orthologous groups of proteins) associated with a phenotype from COG phylogenetic profiles and a phenotype profile. netCAR takes into account the phylogenetic co-occurrence graph between COGs to restrict hypothesis space, and uses mutual information to evaluate the biconditional relation.Results: We examined the mining capability of pairwise and multiple-to-one association by using netCAR to extract COGs relevant to six microbial phenotypes (aerobic, anaerobic, facultative, endospore, motility and Gram negative) from 11 969 unique COG profiles across 155 prokaryotic organisms. With the same level of false discovery rate, multiple-to-one association can extract about 10 times more relevant COGs than one-to-one association. We also reveal various topologies of association networks among COGs (modules) from extracted multiple-to-one correlation rules relevant with the six phenotypes; including a well-connected network for motility, a star-shaped network for aerobic and intermediate topologies for the other phenotypes. netCAR outperforms a standard CAR mining algorithm, CARapriori, while requiring several orders of magnitude less computational time for extracting 3-COG sets.Availability: Source code of the Java implementation is available as Supplementary Material at the Bioinformatics online website, or upon request to the author.Contact: makio323@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

Investigation of Polypeptide Conformational Transitions with Two-Dimensional Raman Optical Activity Correlation Analysis, Applying Autocorrelation and Moving Window Approaches

The study of conformational transitions in polypeptides is not only important for the understanding of folding mechanisms responsible for the self-assembly of proteins but also for the investigation of the misfolding of proteins that can result in diseases including cystic fibrosis, Alzheimer's, and Parkinson's diseases. Our recent studies developing two-dimensional Raman optical activity (ROA) correlation analysis have proven to be successful in the investigation of polypeptide conformational transitions. However, the complexity of the ROA spectra, and the 2D correlation synchronous and asynchronous plots, makes data analysis detailed and complex, requiring great care in interpretation of 2D correlation rules. By utilizing the 2D correlation approaches of autocorrelation and moving windows it has been possible to gain further information from the ROA spectral data sets in a simpler and more consistent way. The most significant spectral intensity changes have been easily identified, facilitating appropriate interpretation of synchronous plots, and transition phases have been identified in the moving window plots, directly relating spectral intensity changes to the perturbation.

Machine condition monitoring using principal component representations

The purpose of this paper is to find the low-dimensional principal component (PC) representations from the statistical features of the measured signals to characterize and hence, monitor machine conditions. The PC representations can be automatically extracted using the principal component analysis (PCA) technique from the time- and frequency-domains statistical features of the measured signals. First, a mean correlation rule is proposed to evaluate the capability of each of the PCs in characterizing machine conditions and to select the most representative PCs to classify machine fault patterns. Then a procedure that uses the low-dimensional PC representations for machine condition monitoring is proposed. The experimental results from an internal-combustion engine sound analysis and an automobile gearbox vibration analysis show that the proposed method is effective for machine condition monitoring.

Presentation of Penrose tiling as set of overlapping pentagonal stars

The layer structure of Penrose tiling in the form of Wieringa roof is considered. It is found that each layer can be described by set of overlapped regular pentagonal stars. Local and global pentagonal symmetry of layers can be vividly presented if the stars will be chosen of less size. Linear size ratio of stars in different layers is equal to golden mean or one. It is offered to use four types of star clusters for layer structure description. List of noticed rules of correlation of star location in different layers is proposed.

A fuzzy statistics based method for mining fuzzy correlation rules

Mining fuzzy association rules is the task of finding the fuzzy itemsets which frequently occur together in large fuzzy dataset, but most proposed methods may identify a fuzzy rule with two fuzzy itemsets as interesting when, in fact, the presence of one fuzzy itemsets in a record does not imply the presence of the other one in the same record. To prevent generating this kind of misleading fuzzy rule, in this paper, we construct a new method for finding relationships between fuzzy itemsets based on fuzzy statistics, and the generated rules are called fuzzy correlation rules. In our method, a fuzzy correlation analysis which can show us the strength and the type of the linear relationship between two fuzzy itemsets is used. By using thus fuzzy statistics analysis, the fuzzy correlation rules with the information about that two fuzzy not only frequently occur together in same records but also are related to each other can be generated.