Representation Of Rules Research Articles

Visualizing Association Rules Using Linked Matrix,Graph, and Detail Views

Although association rule mining is an important pattern recognition and data analysis technique, extracting and finding significant rules from a large collection has always been challenging. The ability of information visualization to enable users to gain an understanding of high dimensional and large-scale data can play a major role in the exploration, identification, and interpretation of association rules. In this paper, we propose a method that provides multiple views of the association rules, linked together through a filtering mechanism. A visual inspection of the entire association rule set is enabled within a matrix view. Items of interest can be selected, resulting in their corresponding association rules being shown in a graph view. At any time, individual rules can be selected in either view, resulting in their information being shown in the detail view. The fundamental premise in this work is that by providing such a visual and interactive representation of the association rules, users will be able to find important rules quickly and easily, even as the number of rules that must be inspected becomes large. A user evaluation was conducted which validates this premise.

Neural representation of rules at different hierarchical levels

Neural representation of rules at different hierarchical levels

CHIRA—Convex Hull Based Iterative Algorithm of Rules Aggregation

In the paper we present CHIRA, an algorithm performing decision rules aggregation. New elementary conditions, which are linear combinations of attributes may appear in rule premises during the aggregation, leading to so-called oblique rules. The algorithm merges rules iteratively, in pairs, according to a certain order specified in advance. It applies the procedure of determining convex hulls for regions in a feature space which are covered by aggregated rules. CHIRA can be treated as the generalization of rule shortening and joining algorithms which, unlike them, allows a rule representation language to be changed. Application of presented algorithm allows one to decrease a number of rules, especially in the case of data in which decision classes are separated by hyperplanes not perpendicular to the attribute axes. Efficiency of CHIRA has been verified on rules obtained by two known rule induction algorithms, RIPPER and q-ModLEM, run on 18 benchmark data sets. Additionally, the algorithm has been applied on synthetic data as well as on a real-life set concerning classification of natural hazards in hard-coal mines.

Fuzzy rule-based similarity model enables learning from small case bases

The concept of similarity plays a fundamental role in case-based reasoning. However, the meaning of “similarity” can vary in situations and is largely domain dependent. This paper proposes a novel similarity model consisting of linguistic fuzzy rules as the knowledge container. We believe that fuzzy rules representation offers a more flexible means to express the knowledge and criteria for similarity assessment than traditional similarity metrics. The learning of fuzzy similarity rules is performed by exploiting the case base, which is utilized as a valuable resource with hidden knowledge for similarity learning. A sample of similarity is created from a pair of known cases in which the vicinity of case solutions reveals the similarity of case problems. We do pair-wise comparisons of cases in the case base to derive adequate training examples for learning fuzzy similarity rules. The empirical studies have demonstrated that the proposed approach is capable of discovering fuzzy similarity knowledge from a rather low number of cases, giving rise to the competence of CBR systems to work on a small case library.

Reflection training improves executive function in preschool-age children: Behavioral and neural effects

To assess the role of reflection in executive function, preschool-age children who perseverated (failed) on a pre-training version of the Dimensional Change Card Sort (DCCS) were given training with a different version (different stimuli) in which they were provided with corrective feedback and taught to reflect on the conflicting rule representations involved in the task. In Exp. 1, reflection training was based closely on Kloo and Perner (2003). Exp. 2 used a shortened (15min) version of the training protocol. In Exp. 3, this version of reflection training was compared to corrective feedback alone or mere practice with the task (without feedback). In all 3 experiments, children who received reflection training showed substantial improvements in performance on the pre-training version of the DCCS, whereas children in control conditions did not. In Exp. 3, these improvements were accompanied by a reduction from pre- to post-training in the amplitude of the N2 component of the ERP, an index of conflict detection. Results suggest not only that EF can be trained using a brief intervention targeting reflection, but also that training-related improvements in performance are associated with the down-regulation of ACC-mediated conflict detection. Implications for education are discussed.

Aplicando o modelo de proporcionalidade degressiva na representação dos Estados na Câmara dos Deputados

This paper investigates the distribution of seats in the House of Representatives among the states. The discussion of this topic usually is based on the representation of a direct proportionality rule between the number of seats and the size of population of each state. This rule introduces a distortion in the representation because of the most populous and least populous states move away from proportional distribution reaches the maximum and the minimum guaranteed by the Federal Constitution. In this case, the ends are underrepresented or over-represented. However, we discuss this issue here in another aspect, from the model of the application of simulation used in the European Union among member countries, with the anxiety of showing another form of distribution of seats in the House from the digressive proportionality applied by the European Parliament , which would reduce the number of parliamentarians, keeping the meaningful representation of the states.

On the neural basis of rule-guided behavior

Human behavior emerges from a complex dynamic interaction between graded and context-sensitive neural processes, the biomechanics of our bodies, and the vicissitudes of our environments. These coupled processes bear little resemblance to the iterated application of simple symbolic rules. Still, there are circumstances under which our behavior appears to be guided by explicit mental rules. A prototypical case is when succinct verbal instructions are communicated and are promptly followed by another. How does the brain support such rule-guided behavior? How are explicit rules represented in the brain? How are rule representations shaped by experience? What neural processes form the foundation of our ability to systematically represent and apply rules from the vast range of possible rules? This article reviews a line of research that has sought a computational cognitive neuroscience account of rule-guided behavior in terms of the functioning of the prefrontal cortex, the basal ganglia, and related brain systems.

Distributed Representations of Rule Identity and Rule Order in Human Frontal Cortex and Striatum

Humans are able to flexibly devise and implement rules to reach their desired goals. For simple situations, we can use single rules, such as "if traffic light is green then cross the street." In most cases, however, more complex rule sets are required, involving the integration of multiple layers of control. Although it has been shown that prefrontal cortex is important for rule representation, it has remained unclear how the brain encodes more complex rule sets. Here, we investigate how the brain represents the order in which different parts of a rule set are evaluated. Participants had to follow compound rule sets that involved the concurrent application of two single rules in a specific order, where one of the rules always had to be evaluated first. The rules and their assigned order were independently manipulated. By applying multivariate decoding to fMRI data, we found that the identity of the current rule was encoded in a frontostriatal network involving right ventrolateral prefrontal cortex, right superior frontal gyrus, and dorsal striatum. In contrast, rule order could be decoded in the dorsal striatum and in the right premotor cortex. The nonhomogeneous distribution of information across brain areas was confirmed by follow-up analyses focused on relevant regions of interest. We argue that the brain encodes complex rule sets by "decomposing" them in their constituent features, which are represented in different brain areas, according to the aspect of information to be maintained.

Mining Method for Weighted Concise Association Rules Based on Closed Itemsets under Weighted Support Framework

Association rules tell us interesting relationships between different items in transaction database. But traditional association rule has two disadvantages. Firstly it assumes every two items have same significance in database, which is unreasonable in many real applications and usually leads to incorrect results. On the other hand, traditional association rule representation contains too much redundancy which makes it difficult to be mined and used. This paper addresses the problem of mining weighted concise association rules based on closed itemsets under weighted support-significant framework, in which each item with different significance is assigned different weight. Through exploiting specific technique, the proposed algorithm can mine all weighted concise association rules while duplicate weighted itemset search space is pruned. As illustrated in experiments, the proposed method leads to good results and achieves good performance.

English to Hindi Paraphrase Convention for Translating Homoeopathy Literature

The rule based approach to machine translation (MT) confines grammatical rules between the source and the target language with the goal of constructing grammatical translation between the language pair. In this paper, we describe the structural representation of English stemmer, POS tagging and design transfer rules which can generate Hindi sentence from the structural representation of the English sentence. Due to the specific terminology of homoeopathic sentences and the linguistic gap between the two languages the translation of these literatures form English to Hindi is a challenging task. The rule sets are used to plug gap between the two languages. Further, rule sets are described for mapping preposition verbs, nouns, etc. Finally, a system architecture has been proposed for the translation of homoeopathy literature from English to Hindi Language.The system accuracy has been evaluated using Bleu score, which is found out to be 0.7501 and the accuracy percentage of the system is 82.23%.

Rules Rule! Brain Activity Dissociates the Representations of Stimulus Contingencies with Varying Levels of Complexity

The significance of stimuli is linked not only to their nature but also to the sequential structure in which they are embedded, which gives rise to contingency rules. Humans have an extraordinary ability to extract and exploit these rules, as exemplified by the role of grammar and syntax in language. To study the brain representations of contingency rules, we recorded ERPs and event-related optical signal (EROS; which uses near-infrared light to measure the optical changes associated with neuronal responses). We used sequences of high- and low-frequency tones varying according to three contingency rules, which were orthogonally manipulated and differed in processing requirements: A Single Repetition rule required only template matching, a Local Probability rule required relating a stimulus to its context, and a Global Probability rule could be derived through template matching or with reference to the global sequence context. ERP activity at 200-300 msec was related to the Single Repetition and Global Probability rules (reflecting access to representations based on template matching), whereas longer-latency activity (300-450 msec) was related to the Local Probability and Global Probability rules (reflecting access to representations incorporating contextual information). EROS responses with corresponding latencies indicated that the earlier activity involved the superior temporal gyrus, whereas later responses involved a fronto-parietal network. This suggests that the brain can simultaneously hold different models of stimulus contingencies at different levels of the information processing system according to their processing requirements, as indicated by the latency and location of the corresponding brain activity.

Synthesizing software verifiers from proof rules

Automatically generated tools can significantly improve programmer productivity. For example, parsers and dataflow analyzers can be automatically generated from declarative specifications in the form of grammars, which tremendously simplifies the task of implementing a compiler. In this paper, we present a method for the automatic synthesis of software verification tools. Our synthesis procedure takes as input a description of the employed proof rule, e.g., program safety checking via inductive invariants, and produces a tool that automatically discovers the auxiliary assertions required by the proof rule, e.g., inductive loop invariants and procedure summaries. We rely on a (standard) representation of proof rules using recursive equations over the auxiliary assertions. The discovery of auxiliary assertions, i.e., solving the equations, is based on an iterative process that extrapolates solutions obtained for finitary unrollings of equations. We show how our method synthesizes automatic safety and liveness verifiers for programs with procedures, multi-threaded programs, and functional programs. Our experimental comparison of the resulting verifiers with existing state-of-the-art verification tools confirms the practicality of the approach.

Compositionality of Rule Representations in Human Prefrontal Cortex

Rules are widely used in everyday life to organize actions and thoughts in accordance with our internal goals. At the simplest level, single rules can be used to link individual sensory stimuli to their appropriate responses. However, most tasks are more complex and require the concurrent application of multiple rules. Experiments on humans and monkeys have shown the involvement of a frontoparietal network in rule representation. Yet, a fundamental issue still needs to be clarified: Is the neural representation of multiple rules compositional, that is, built on the neural representation of their simple constituent rules? Subjects were asked to remember and apply either simple or compound rules. Multivariate decoding analyses were applied to functional magnetic resonance imaging data. Both ventrolateral frontal and lateral parietal cortex were involved in compound representation. Most importantly, we were able to decode the compound rules by training classifiers only on the simple rules they were composed of. This shows that the code used to store rule information in prefrontal cortex is compositional. Compositional coding in rule representation suggests that it might be possible to decode other complex action plans by learning the neural patterns of the known composing elements.

Layered interval codes for TCAM-based classification

Ternary content-addressable memories (TCAMs) are increasingly used for high-speed packet classification. TCAMs compare packet headers against all rules in a classification database in parallel and thus provide high throughput.TCAMs are not well-suited, however, for representing rules that contain range fields and previously published algorithms typically represent each such rule by multiple TCAM entries. The resulting range expansion can dramatically reduce TCAM utilization because it introduces a large number of redundant TCAM entries. This redundancy can be mitigated by making use of extra bits, available in each TCAM entry.We present a scheme for constructing efficient representations of range rules, based on the simple observation that sets of disjoint ranges may be encoded much more efficiently than sets of overlapping ranges. Since the ranges in real-world classification databases are, in general, non-disjoint, the algorithms we present split ranges between multiple layers, each of which consists of mutually disjoint ranges. Each layer is then coded and assigned its own set of extra bits.Our layering algorithms are based on approximations for specific variants of interval-graph coloring. We evaluate these algorithms by performing extensive comparative analysis on real-life classification databases. Our analysis establishes that our algorithms reduce the number of redundant TCAM entries caused by range rules by more than 60% as compared with best range-encoding prior work.

Correspondence between quantum-optical transform and classical-optical transform explored by developing Dirac’s symbolic method

By virtue of the new technique of performing integration over Dirac’s ket-bra operators, we explore quantum optical version of classical optical transformations such as optical Fresnel transform, Hankel transform, fractional Fourier transform, Wigner transform, wavelet transform and Fresnel-Hadmard combinatorial transform etc. In this way one may gain benefit for developing classical optics theory from the research in quantum optics, or vice-versa. We cannot only find some new quantum mechanical unitary operators which correspond to the known optical transformations, deriving a new theorem for calculating quantum tomogram of density operators, but also can reveal some new classical optical transformations. For examples, we find the generalized Fresnel operator (GFO) to correspond to the generalized Fresnel transform (GFT) in classical optics. We derive GFO’s normal product form and its canonical coherent state representation and find that GFO is the loyal representation of symplectic group multiplication rule. We show that GFT is just the transformation matrix element of GFO in the coordinate representation such that two successive GFTs is still a GFT. The ABCD rule of the Gaussian beam propagation is directly demonstrated in the context of quantum optics. Especially, the introduction of quantum mechanical entangled state representations opens up a new area in finding new classical optical transformations. The complex wavelet transform and the condition of mother wavelet are studied in the context of quantum optics too. Throughout our discussions, the coherent state, the entangled state representation of the two-mode squeezing operators and the technique of integration within an ordered product (IWOP) of operators are fully used. All these have confirmed Dirac’s assertion: “...for a quantum dynamic system that has a classical analogue, unitary transformation in the quantum theory is the analogue of contact transformation in the classical theory”.

Flexible rule use: Common neural substrates in children and adults

Flexible rule-guided behavior develops gradually, and requires the ability to remember the rules, switch between them as needed, and implement them in the face of competing information. Our goals for this study were twofold: first, to assess whether these components of rule-guided behavior are separable at the neural level, and second, to identify age-related differences in one or more component that could support the emergence of increasingly accurate and flexible rule use over development. We collected event-related fMRI data while 36 children aged 8–13 and adults aged 20–27 performed a task that manipulated rule representation, rule switching, and stimulus incongruency. Several regions – left dorsolateral prefrontal cortex (DLPFC), left posterior parietal cortex, and pre-supplementary motor area – were engaged by both the rule representation and the rule-switching manipulations. These regions were engaged similarly across age groups, though contrasting timecourses of activation in left DLPFC suggest that children updated task rules more slowly than did adults. These findings support the idea that common networks can contribute to a variety of executive functions, and that some developmental changes take the form of changes in temporal dynamics rather than qualitative changes in the network of brain regions engaged.

Fiscal and Monetary Policy Interaction: A Simulation-Based Analysis of a Two-Country New Keynesian DSGE Model with Heterogeneous Households

This paper models a fiscal policy that pursues primary balance targets to stabilize the debt-to-GDP ratio in an open and heterogeneous economy where firms combine public and private capital to produce their goods. The model extends the European NAWM presented in Coenen et al. (2008) and Christoffel et al. (2008) by broadening the scope for fiscal policy implementation and allowing for heterogeneity in labor skills. The domestic economy is also assumed to follow a forward looking Taylor-rule consistent with an inflation targeting regime. We correct the NAWM specification of the final-goods price indices, the recursive representation of the wage setting rule, and the wage distortion index. We calibrate the model for Brazil to analyze some implications of monetary and fiscal policy interaction and explore some of the implications of fiscal policy in this class of DSGE models.

Space-Efficient TCAM-Based Classification Using Gray Coding

Ternary content-addressable memories (TCAMs) are increasingly used for high-speed packet classification. TCAMs compare packet headers against all rules in a classification database in parallel and thus provide high throughput unparalleled by software-based solutions. TCAMs are not well-suited, however, for representing rules that contain range fields. Such rules typically have to be represented (or encoded) by multiple TCAM entries. The resulting range expansion can dramatically reduce TCAM utilization. A TCAM range-encoding algorithm A is database-independent if, for all ranges r, it encodes r independently of the database in which it appears; otherwise, we say that A is database-dependent. Typically, when storing a classification database in TCAM, a few dozens of so-called extra bits in each TCAM entry remain unused. These extra bits are used by some (both database-dependent and database-independent) prior algorithms to reduce range expansion. The majority of real-life database ranges are short. We present a novel database-independent algorithm called Short Range Gray Encoding (SRGE) for the efficient representation of short range rules. SRGE encodes range endpoints as binary-reflected Gray codes and then represents the resulting range by a minimal set of ternary strings. To the best of our knowledge, SRGE is the first algorithm that achieves a reduction in range expansion in general, and a significant expansion reduction for short ranges in particular, without resorting to the use of extra bits. The “traditional” database-independent technique for representing range entries in TCAM is prefix expansion. As we show, SRGE significantly reduces the expansion of short ranges in comparison with prefix expansion. We also prove that the SRGE algorithm's range expansion is at least as good as that of prefix expansion for any range. Real-world classification databases contain a small number of unique long ranges, some of which appear in numerous rules. These long ranges cause high expansion which is not significantly reduced by any database-independent range encoding scheme that we are aware of, including SRGE. We introduce hybrid SRGE, a database-dependent encoding scheme that uses SRGE for reducing the expansion of short ranges and uses extra bits for reducing the expansion caused by long ones. Our comparative analysis establishes that hybrid SRGE utilizes TCAM more efficiently than previously published range-encoding algorithms. This work also makes a more theoretic contribution. Prefix expansion for ranges defined by W-bit endpoints has worst-case expansion ratio of 2W-2. It follows from the work of Schieber et al. [1] that the SRGE algorithm has a slightly better worst-case expansion ratio of 2W-4. We prove that any independent TCAM encoding scheme has worst-case expansion ratio of at least W.

BRACID: a comprehensive approach to learning rules from imbalanced data

In this paper we consider induction of rule-based classifiers from imbalanced data, where one class (a minority class) is under-represented in comparison to the remaining majority classes. The minority class is usually of primary interest. However, most rule-based classifiers are biased towards the majority classes and they have difficulties with correct recognition of the minority class. In this paper we discuss sources of these difficulties related to data characteristics or to an algorithm itself. Among the problems related to the data distribution we focus on the role of small disjuncts, overlapping of classes and presence of noisy examples. Then, we show that standard techniques for induction of rule-based classifiers, such as sequential covering, top-down induction of rules or classification strategies, were created with the assumption of balanced data distribution, and we explain why they are biased towards the majority classes. Some modifications of rule-based classifiers have been already introduced, but they usually concentrate on individual problems. Therefore, we propose a novel algorithm, BRACID, which more comprehensively addresses the issues associated with imbalanced data. Its main characteristics includes a hybrid representation of rules and single examples, bottom-up learning of rules and a local classification strategy using nearest rules. The usefulness of BRACID has been evaluated in experiments on several imbalanced datasets. The results show that BRACID significantly outperforms the well known rule-based classifiers C4.5rules, RIPPER, PART, CN2, MODLEM as well as other related classifiers as RISE or K-NN. Moreover, it is comparable or better than the studied approaches specialized for imbalanced data such as generalizations of rule algorithms or combinations of SMOTE + ENN preprocessing with PART. Finally, it improves the support of minority class rules, leading to better recognition of the minority class examples.

Design of the CNC Milling Tool Selection System

Knowledge base of milling feature machining based on relational database is proposed, using knowledge representation of production rules, according to the characteristics of feature machining knowledge. Tool selected reasoning mechanism and reasoning processes is presented basing the installed CNC milling tool database , and further reasoning is based on knowledge, milling tool selection method is implemented based on feature machining knowledge using the forward direction inference strategy .In the paper, a complete system of the selecting milling cutter is established. The system connects not only the theoretical knowledge but the expert’s experiences with the computer applications in order to provide a base of realizing the automatic mechanical processing. By the example of machining tool selection for complex surface, the selection process is described, and the system can select the tools to meet the processing requirements within a shot time, and has good versatility.