scholarly journals Efficient Breadth-First Reduct Search

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 833
Author(s):  
Veera Boonjing ◽  
Pisit Chanvarasuth
Keyword(s):  
Information System ◽  
Search Strategy ◽  
Search Space ◽  
Graph Search ◽  
Search Problem ◽  
Breadth First Search ◽  
Time And Space ◽  
First Order ◽  
Pruning Rule

This paper formulates the problem of determining all reducts of an information system as a graph search problem. The search space is represented in the form of a rooted graph. The proposed algorithm uses a breadth-first search strategy to search for all reducts starting from the graph root. It expands nodes in breadth-first order and uses a pruning rule to decrease the search space. It is mathematically shown that the proposed algorithm is both time and space efficient.

INDEX-MAXMINER: A NEW MAXIMAL FREQUENT ITEMSET MINING ALGORITHM

2008 ◽  
Vol 17 (02) ◽  
pp. 303-320 ◽  
Author(s):  
WEI SONG ◽  
BINGRU YANG ◽  
ZHANGYAN XU
Keyword(s):  
Search Strategy ◽  
Search Space ◽  
Frequent Itemset ◽  
Breadth First Search ◽  
Hybrid Search ◽  
Itemset Mining ◽  
Frequent Item ◽  
Enumeration Tree ◽  
Frequent Items ◽  
Mining Algorithms

Because of the inherent computational complexity, mining the complete frequent item-set in dense datasets remains to be a challenging task. Mining Maximal Frequent Item-set (MFI) is an alternative to address the problem. Set-Enumeration Tree (SET) is a common data structure used in several MFI mining algorithms. For this kind of algorithm, the process of mining MFI's can also be viewed as the process of searching in set-enumeration tree. To reduce the search space, in this paper, a new algorithm, Index-MaxMiner, for mining MFI is proposed by employing a hybrid search strategy blending breadth-first and depth-first. Firstly, the index array is proposed, and based on bitmap, an algorithm for computing index array is presented. By adding subsume index to frequent items, Index-MaxMiner discovers the candidate MFI's using breadth-first search at one time, which avoids first-level nodes that would not participate in the answer set and reduces drastically the number of candidate itemsets. Then, for candidate MFI's, depth-first search strategy is used to generate all MFI's. Thus, the jumping search in SET is implemented, and the search space is reduced greatly. The experimental results show that the proposed algorithm is efficient especially for dense datasets.

Automated Design of Image Operators that Detect Interest Points

2008 ◽  
Vol 16 (4) ◽  
pp. 483-507 ◽  
Author(s):  
Leonardo Trujillo ◽  
Gustavo Olague
Keyword(s):  
Computer Vision ◽  
Search Space ◽  
Search Problem ◽  
Interest Points ◽  
Fitness Evaluation ◽  
Modern Computer ◽  
New Perspective ◽  
Processing Techniques ◽  
Point Detection ◽  
Optimization Search

This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.

scholarly journals Probabilistic Inference for Predicate Constraint Satisfaction

2020 ◽  
Vol 34 (02) ◽  
pp. 1644-1651
Author(s):  
Yuki Satake ◽  
Hiroshi Unno ◽  
Hinata Yanagi
Keyword(s):  
Graphical Models ◽  
Constraint Satisfaction ◽  
Linear Time ◽  
Predicate Logic ◽  
Probabilistic Inference ◽  
Search Space ◽  
Constraint Satisfaction Problems ◽  
Safety Properties ◽  
First Order ◽  
Guided Inductive

In this paper, we present a novel constraint solving method for a class of predicate Constraint Satisfaction Problems (pCSP) where each constraint is represented by an arbitrary clause of first-order predicate logic over predicate variables. The class of pCSP properly subsumes the well-studied class of Constrained Horn Clauses (CHCs) where each constraint is restricted to a Horn clause. The class of CHCs has been widely applied to verification of linear-time safety properties of programs in different paradigms. In this paper, we show that pCSP further widens the applicability to verification of branching-time safety properties of programs that exhibit finitely-branching non-determinism. Solving pCSP (and CHCs) however is challenging because the search space of solutions is often very large (or unbounded), high-dimensional, and non-smooth. To address these challenges, our method naturally combines techniques studied separately in different literatures: counterexample guided inductive synthesis (CEGIS) and probabilistic inference in graphical models. We have implemented the presented method and obtained promising results on existing benchmarks as well as new ones that are beyond the scope of existing CHC solvers.

Limiting the Search Space during Controls Evaluation of a Modified Information System

2017 ◽  
Vol 32 (3) ◽  
pp. 91-111
Author(s):  
Yair Wand ◽  
Ron Weber
Keyword(s):  
Information System ◽  
Quality Control ◽  
Information Systems ◽  
Level Structure ◽  
Search Space ◽  
Requirements Specifications ◽  
System 2 ◽  
New System

ABSTRACT Controls must be changed when information systems are modified. Audit, assurance, and quality-control (AAQC) personnel must evaluate the reliability of controls in the new system versions. Based on Bunge (1977, 1979) and Wand and Weber (1989a, 1990), we describe a model and search-space algorithm that AAQC personnel can use to determine where required control changes are likely to be located in the new system version, thereby mitigating the need for an exhaustive evaluation of all controls. To use the model and algorithm, AAQC personnel must have (1) accurate and complete requirements specifications for the old and new versions of the system, (2) a controls specification for the old version that covers all errors and irregularities that might occur, (3) evidence to conclude all controls for the old version are in place, adequate, and working, and (4) specifications for the new version expressed as a level structure of systems and subsystems.

scholarly journals Learning Descriptors Invariance through Equivalence Relations within Manifold: A New Approach to Expression Invariant 3D Face Recognition

2020 ◽  
Vol 6 (11) ◽  
pp. 112
Author(s):  
Faisal R. Al-Osaimi
Keyword(s):  
Search Algorithm ◽  
Recognition Performance ◽  
Feature Space ◽  
Training Data ◽  
Equivalence Relations ◽  
Graph Search ◽  
Search Problem ◽  
New Approach ◽  
Unique Approach ◽  
Training Examples

This paper presents a unique approach for the dichotomy between useful and adverse variations of key-point descriptors, namely the identity and the expression variations in the descriptor (feature) space. The descriptors variations are learned from training examples. Based on labels of the training data, the equivalence relations among the descriptors are established. Both types of descriptor variations are represented by a graph embedded in the descriptor manifold. Invariant recognition is then conducted as a graph search problem. A heuristic graph search algorithm suitable for the recognition under this setup was devised. The proposed approach was tested on the FRGC v2.0, the Bosphorus and the 3D TEC datasets. It has shown to enhance the recognition performance, under expression variations, by considerable margins.

External Knowledge Search Strategy as an Essential Element of a Knowledge Management Strategy

2011 ◽  
pp. 70-89
Author(s):  
Fergal McGrath ◽  
Rebecca Purcell
Keyword(s):  
Knowledge Management ◽  
Management Strategy ◽  
Search Strategy ◽  
Central Element ◽  
Essential Element ◽  
Search Space ◽  
Knowledge Management Strategy ◽  
External Knowledge ◽  
Knowledge Search ◽  
The Many

This chapter introduces external knowledge search strategy as a central element of an organizations overall knowledge management strategy. The argument cites how knowledge management has developed around a myopic internal focus and has thus far failed to take full account of the many sources of knowledge external to the organization. The chapter offers external knowledge search strategy as a means of integrating this external focus into knowledge management understanding, by providing a conceptual framework for organizations involved in the external knowledge management activity of external knowledge search. The framework identifies 10 search paths organizations may follow into the search space, four of which relate exclusively to external knowledge search. The authors hope that establishing an external element within knowledge management strategy will inform knowledge management’s recognition of the value of the extended enterprise.

Motion Planning for Dynamic Systems Using First-Order Logic

2002 ◽  
Author(s):  
Lei Yan ◽  
K. Krishnamurthy
Keyword(s):  
Motion Planning ◽  
Dynamic Systems ◽  
Initial Conditions ◽  
Search Space ◽  
Order Logic ◽  
First Order Logic ◽  
Knowledge Representation And Reasoning ◽  
Know How ◽  
First Order ◽  
Knowledge Based

The problem of motion planning for a class of dynamic systems is considered in this study. A knowledge-based approach is used to determine the initial conditions that will yield a certain desired state of the dynamic system. The search space is limited by using a set of rules because reasoning about dynamic systems is basically searching an infinite space. In this study, first-order logic is used for knowledge representation and reasoning. The methodology is applied to playing a pool game. The dynamics of the motion of the balls are complicated and significant expertise is required to know how to strike the balls. Simulated results presented show how the rules help in finding the appropriate strategies for playing the game.

scholarly journals SETI: The Microwave Search Problem and the Nasa Sky Survey Approach

1985 ◽  
Vol 112 ◽  
pp. 397-403
Author(s):  
Michael J. Klein ◽  
Samuel Gulkis
Keyword(s):  
Search Strategy ◽  
High Sensitivity ◽  
Search Problem ◽  
Dual Mode ◽  
Ongoing Work ◽  
Sky Survey ◽  
Frequency Coverage ◽  
Complementary Nature ◽  
Low Sensitivity ◽  
Survey Approach

NASA's microwave observing program for SETI is presented. This strategy is composed of a high sensitivity, narrow frequency coverage, Target Search and a low sensitivity, broad frequency coverage, Sky Survey. The complementary nature of this dual mode search strategy is discussed. An overview is given of ongoing work in the development of the search strategy for the Sky Survey.

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