Item Constraints Research Articles

A Novel Constraint-Aware Flexible Model with Ant Colony Optimization for Symmetrical Travel Recommendation

This paper proposes a flexible travel recommender model (FTRM) that emphasizes the symmetry between user preferences and travel constraints, addressing key challenges in the field such as the integration of diverse constraint types and the customization of travel itineraries. The key contribution of the proposed model lies in its integration with the item constraints data model (ICDM), which effectively manages a plethora of constraint types. Additionally, this study develops a novel algorithm inspired by ant colony optimization (ACO) principles, demonstrating performance metrics that are comparable to state-of-the-art algorithms in this field. A comprehensive set of systematic experimental analyses is conducted, employing various models across diverse situational contexts, with the primary goal of illustrating the capabilities of the proposed symmetrical FTRM using real-world data from the Durham dataset. The obtained results highlight the model’s ability to accommodate diverse constraint types, facilitating the customization of travel itineraries to suit individual user preferences and achieve a balanced and symmetrical travel experience. Specifically, our model outperforms existing models in terms of flexibility and customization, showing significant improvements in user satisfaction and itinerary efficiency.

Constraint-based high utility mobile trajectory pattern mining for security systems

In security system, high utility pattern mining of a large number of users mobile trajectories is helpful to analyze user behavior patterns, and enhance the internal prevention of the security system.Currently, the frequent pattern mining for mobile trajectory in security systems do not take into account the differences in the safety levels between staying points and the frequency of their occurrence. Existing high utility mobile trajectory pattern mining methods are unable to discover staying points that meet specific user requirements. To solve these problems, a constraint-based high utility mobile trajectory mining algorithm is proposed (names as HUIM-ILC-ACO). It takes into account the user’s stay time, stay frequency, and the safety level of each stopping point and calculates the utility value of each staying point by incorporating these factors through weighted computation. Based on this, the algorithm combines ant colony optimization with length constraints and item constraints to construct a method for mining high utility mobile trajectory patterns that better align with user interests. Experimental results on real datasets and a target mobile trajectory RFID dataset show that proposed algorithm is efficient in terms of runtime and pattern quantity, and it can effectively mine a pattern set that is closer to user interests.

Research on the Application of Apriori Algorithm in the Teaching of Ball Sports Techniques and Tactics

To address the problem that technical and tactical aspects of ball games have a large impact on match-winning, association rules are used to mine and analyse the data. Item constraint terms are added to the original Apriori algorithm to reduce the generation of redundant data, and the operational efficiency of the improved association rules is improved with the aid of the GSA-PSO algorithm to give full play to the fast convergence advantage of the hybrid algorithm. The experimental results show that the algorithm proposed in the study is more convergent than the Apriori algorithm, and the former has fewer iterations on average in the single-peaked function; the overall search accuracy is significantly improved. The algorithm is also less disturbed by the dataset than the Apriori algorithm, with an average running time of 19.23s, 21.54s and 25.61s for dataset sizes of 200, 500 and 1000 respectively. The deviation rate between the predicted and actual coaching strategies was less than 5% and the scoring tolerance rate was between 0.04 and 0.10. It indicates that the algorithm proposed in the study can improve the efficiency of data analysis, which is conducive to the development of a perfect ball sport technical and tactical scheme and promote the stability of ball sport technical and tactical development.

P-IOC-FPMine: an association rule mining algorithm for item constraints based on the MapReduce framework

P-IOC-FPMine: an association rule mining algorithm for item constraints based on the MapReduce framework

P-IOC-FPMine: an association rule mining algorithm for item constraints based on the MapReduce framework

In the application of big data, one of the most challenging problems is how to consider the requirements of users. To avoid this problem, we proposed IOC-FP-growth. This added user-defined pre-term or post-item constraints into the classic FP-growth algorithm. What's more, a parallel data mining algorithm based on MapReduce, namely P-IOC-FPMine was proposed, which was a low-memory fast association rule mining algorithm. Finally, by evaluating the effectiveness of the method in the public data. The results showed that the IOC-FP-growth method can consider the user's needs for association rules easily. Compared with the FP-growth, Recorder and PNARCMC, we only need to meet the requirements of users to extract accurate rules, which will bring huge advantages for data mining. After parallelisation, the performance of the P-IOC-FPMine was better than FP-growth on the data set. The results showed that the P-IOC-FPMine was more appropriate for handling large-scale data sets.

Efficient algorithms for mining frequent high utility sequences with constraints

An important data mining task is to discover all high utility sequences in a quantitative sequence database. Although useful, the number of discovered sequences is often very large. To find patterns that are more tailored to a user’s needs, this paper studies the problem of mining frequent high utility sequences satisfying item constraints. This article proposes a novel algorithm named C-FHUSM to quickly obtain these sequences from two concise representations discovered from a quantitative sequence database, namely frequent generator high utility sequences and frequent closed high utility sequences. The first set is extracted using a novel algorithm named FGenHUSM, while an existing algorithm is applied to extract the second set. C-FHUSM integrates novel pruning techniques to ignore sequences that do not satisfy item constraints early by checking only a small number of representative sequences at the beginning of the mining process. Experimental results show that C-FHUSM can be more than ten times faster and has better scalability than a modified version of the state-of-the-art EHUSM algorithm for mining sequences with item constraints. Moreover, it is found that using C-FHUSM is beneficial when a user frequently changes constraints as results can be updated without rescanning the database.

Test Assembly for Cognitive Diagnosis Using Mixed-Integer Linear Programming

One purpose of cognitive diagnostic model (CDM) is designed to make inferences about unobserved latent classes based on observed item responses. A heuristic for test construction based on the CDM information index (CDI) proposed by Henson and Douglas (2005) has a far-reaching impact, but there are still many shortcomings. He and other researchers had also proposed new methods to improve or overcome the inherent shortcomings of the CDI test assembly method. In this study, one test assembly method of maximizing the minimum inter-class distance is proposed by using mixed-integer linear programming, which aims to overcome the shortcomings that the CDI method is limited to summarize the discriminating power of each item into a single CDI index while neglecting the discriminating power for each pair of latent classes. The simulation results show that compared with the CDI test assembly and random test assembly, the new test assembly method performs well and has the highest accuracy rate in terms of pattern and attributes correct classification rates. Although the accuracy rate of the new method is not very high under item constraints, it is still higher than the CDI test assembly with the same constraints.

Fast generation of sequential patterns with item constraints from concise representations

Constraint-based frequent sequence mining is an important and necessary task in data mining since it shows results very close to the requirements and interests of users. Most existing algorithms for performing this task are based on a traditional approach that mines patterns directly from a sequence database (SDB). However, in fact, SDBs are often very large. The algorithms thus often exhibit poor performance because the number of generated candidates and the search space are enormous, especially for low minimum support thresholds. In addition, these algorithms must read an SDB again when a constraint is changed by the user. In the context of frequently varied constraints, repeatedly scanning SDBs consume much time. To address this issue, we propose a novel approach for generating frequent sequences with various constraints from the two sets of frequent closed sequences ($$ {{\mathcal{F}}{\mathcal{C}}{\mathcal{S}}} $$) and frequent generator sequences ($$ {{\mathcal{F}}{\mathcal{G}}{\mathcal{S}}} $$), which are the concise representations of the set $$ {{\mathcal{F}}{\mathcal{S}}} $$ of all frequent sequences. The proposed approach is based on novel theoretical results that show an explicit relationship between $$ {{\mathcal{F}}{\mathcal{S}}} $$ and these two sets and have been strictly proved. The approach is then used to develop an efficient algorithm named MFS-IC for quickly generating frequent sequences with item constraints, a task that has many real-life applications. Extensive experiments on real-life and synthetic databases show that the proposed MFS-IC algorithm outperforms state-of-the-art algorithms, which directly mine frequent sequences with constraints from an SDB, in terms of runtime, memory usage and scalability.

Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified.

RETRACTED: Discovery of Characteristic Sequential Patterns Based on Two Types of Constraints

This article proposes a method for discovering characteristic sequential patterns from sequential data by using background knowledge. In the case of the tabular structured data, each item is composed of an attribute and an attribute value. This article focuses on two types of constraints describing background knowledge. The first one is time constraints. It can flexibly describe relationships related to the time between items. The second one is item constraints, it can select items included in sequential patterns. These constraints can represent the background knowledge representing the interests of analysts. Therefore, they can easily discover sequential patterns coinciding the interests as characteristic sequential patterns. Lastly, this article verifies the effect of the pattern discovery method based on both the evaluation criteria of sequential patterns and the background knowledge. The method can be applied to the analysis of the healthcare data.

Agreement on the Classification of Latent Class Membership Between Different Identification Constraint Approaches in the Mixture Rasch Model

Abstract. When using the mixture Rasch model, the model identification constraints are either to set the equal means for all classes in the assumed normal ability distributions (equal ability mean constraint in short), or to set the sum of item difficulties to be zero for each class. In real data analysis, however, both constraints are not always sufficient to establish a common scale across latent classes unless some items are specified as anchor items in the estimation. If these two conventional constraint approaches recover the class membership as good as the anchor item constraint approach, the conventional constraint approaches may be considered useful for the purpose of class membership classification. This study investigated agreement on class membership between one conventional constraint (the equal ability mean) and the anchor item constraint approaches. Results showed high agreement between these two constraint approaches, indicating that the conventional constraint of the equal mean ability approach may be used to recover the latent class membership although item profiles are not correctly estimated across latent classes.

Mining constrained inter-sequence patterns: a novel approach to cope with item constraints

Data mining has become increasingly important in the Internet era. The problem of mining inter-sequence pattern is a sub-task in data mining with several algorithms in the recent years. However, these algorithms only focus on the transitional problem of mining frequent inter-sequence patterns and most frequent inter-sequence patterns are either redundant or insignificant. As such, it can confuse end users during decision-making and can require too much system resources. This led to the problem of mining inter-sequence patterns with item constraints, which addressed the problem when end-users only concerned the patterns contained a number of specific items. In this paper, we propose two novel algorithms for it. First is the ISP-IC (Inter-Sequence Pattern with Item Constraint mining) algorithm based on a theorem that quickly determines whether an inter-sequence pattern satisfies the constraints. Then, we propose a way to improve the strategy of ISP-IC, which is then applied to the $i$ ISP-IC algorithm to enhance the performance of the process. Finally, pi ISP-IC, a parallel version of $i$ ISP-IC, will be presented. Experimental results show that pi ISP-IC algorithm outperforms the post-processing of the-state-of-the-art method for mining inter-sequence patterns (EISP-Miner), ISP-IC, and $i$ ISP-IC algorithms in most of the cases.

The influence of cloze probability and item constraint on cloze task response time

Abstract In research on the role of lexical predictability in language comprehension, predictability is generally defined as the probability that a word is provided as a sentence continuation in the cloze task (Taylor, 1953), in which subjects are asked to guess the next word of a sentence. The present experiments investigate the process by which subjects generate a cloze response, by measuring the latency to initiate a response in a version of the task in which subjects produce a spoken continuation to a visually presented sentence fragment. Higher probability responses were produced faster than lower probability responses. The latency to produce a response was also influenced by item constraint: A response at a given level of probability was issued faster when the context was more constraining, i.e., a single response was elicited with high probability. We show that these patterns are naturally produced by an activation-based race model in which potential responses independently race towards a response threshold. Implications for the interpretation of cloze probability as a measure of lexical predictability are discussed.

Buying Scenario and Recommendation of Purchase by Constraint Based Sequential Pattern Mining from Time Stamp Based Sequential Dataset

In today's competitive world, one of the most important drivers for sustainability and growth of a business is retention of customer. For retention of customer, it is important to understand customers’ current needs and wants. Understanding of customers’ buying behavior helps in designing products by businesses for ensuring product attributes that will result in customer satisfaction on purchase and consumption. Proposed work mainly focuses on prediction of few items that customer is in all probability more likely to purchase in future than other items based on data analysis done using sequential pattern mining technique. Proposed work additionally focuses on recommendations to be made for items of purchase in near and long term future, based on incorporation of Gap constraint in conventional FP-growth based PrefixSpan algorithm. Combination of Length, Gap and Item constraints help to recommend next highly probable items for purchase, based on increasing support and confidence values. Simulation and experimental study done on six IBM generated synthetic dataset which throw light on customers’ buying patterns in terms of purchases that happened regularly or happened just once. Average 45%-55% purchase happens only once which doesn’t lead to probability of customer making next purchase (IBM generated synthetic dataset). Proposed system focuses on next purchase item prediction based on support, confidence and gap constraint. Decision maker can analyze the reasons of current situation and accordingly make vital recommendations in terms of next products to be offered to customer for purchase, thereby increasing the probability of sales actually happening.

Mining association rules with negative item constraints.

Mining association rules with negative item constraints.

Research on Knowledge Fusion Based Parametric Design Technology of the Sliding Screw Mechanism

The simultaneous application of Logical reasoning and model design in parametric design technology based on Knowledge fusion in the interactive process of CAD system, makes design process are creative; its key technology includes the building of knowledge model base, the reasonable representation of knowledge items and knowledge constraints, correct establishment of the relation among knowledge item and knowledge constraint reference variable; the paper studies the key technology in parametric design based on Knowledge fusion, expresses the application in sliding screw mechanism of the software based on the key technology, and finally gives an example of parametric design technology of sliding screw mechanism based on Knowledge fusion.

Mining interesting imperfectly sporadic rules

Detecting association rules with low support but high confidence is a difficult data mining problem. To find such rules using approaches like the Apriori algorithm, minimum support must be set very low, which results in a large number of redundant rules. We are interested in sporadic rules; i.e. those that fall below a maximum support level but above the level of support expected from random coincidence. There are two types of sporadic rules: perfectly sporadic and imperfectly sporadic. Here we are more concerned about finding imperfectly sporadic rules, where the support of the antecedent as a whole falls below maximum support, but where items may have quite high support individually. In this paper, we introduce an algorithm called Mining Interesting Imperfectly Sporadic Rules (MIISR) to find imperfectly sporadic rules efficiently, e.g. fever, headache, stiff neck → meningitis. Our proposed method uses item constraints and coincidence pruning to discover these rules in reasonable time. This paper is an expanded version of Koh et al. [Advances in knowledge discovery and data mining: 10th Pacific-Asia Conference (PAKDD 2006), Singapore. Lecture Notes in Computer Science 3918, Springer, Berlin, pp 473–482].

Mining frequent patterns for AMP-activated protein kinase regulation on skeletal muscle.

BackgroundAMP-activated protein kinase (AMPK) has emerged as a significant signaling intermediary that regulates metabolisms in response to energy demand and supply. An investigation into the degree of activation and deactivation of AMPK subunits under exercise can provide valuable data for understanding AMPK. In particular, the effect of AMPK on muscle cellular energy status makes this protein a promising pharmacological target for disease treatment. As more AMPK regulation data are accumulated, data mining techniques can play an important role in identifying frequent patterns in the data. Association rule mining, which is commonly used in market basket analysis, can be applied to AMPK regulation.ResultsThis paper proposes a framework that can identify the potential correlation, either between the state of isoforms of α, β and γ subunits of AMPK, or between stimulus factors and the state of isoforms. Our approach is to apply item constraints in the closed interpretation to the itemset generation so that a threshold is specified in terms of the amount of results, rather than a fixed threshold value for all itemsets of all sizes. The derived rules from experiments are roughly analyzed. It is found that most of the extracted association rules have biological meaning and some of them were previously unknown. They indicate direction for further research.ConclusionOur findings indicate that AMPK has a great impact on most metabolic actions that are related to energy demand and supply. Those actions are adjusted via its subunit isoforms under specific physical training. Thus, there are strong co-relationships between AMPK subunit isoforms and exercises. Furthermore, the subunit isoforms are correlated with each other in some cases. The methods developed here could be used when predicting these essential relationships and enable an understanding of the functions and metabolic pathways regarding AMPK.

Vague knowledge search in the design for outsourcing using fuzzy decision tree

One of the most popular strategies in business today is global outsourcing. The information which needs to be provided for outsourcing and obtained from outsourced maintenance during the R&D processes is very important for the successful development of a product. Although DFX plays a key role in the R&D processes by considering the important X item constraints simultaneously in the design stage, the vague outsourced maintenance data and knowledge are seldom properly analyzed and used. In this study, fuzzy decision tree is used to form a search mechanism for vague knowledge in design for outsourcing (DFO) with index for classifying vague knowledge. Computational experiments were conducted to demonstrate the performance of the proposed search mechanism and knowledge classification.

Mining frequent itemsets without support threshold: with and without item constraints

In classical association rules mining, a minimum support threshold is assumed to be available for mining frequent itemsets. However, setting such a threshold is typically hard. We handle a more practical problem; roughly speaking, it is to mine N k-itemsets with the highest supports for k up to a certain k/sub max/ value. We call the results the N-most interesting itemsets. Generally, it is more straightforward for users to determine N and k/sub max/. We propose two new algorithms, LOOPBACK and BOMO. Experiments show that our methods outperform the previously proposed Itemset-Loop algorithm, and the performance of BOMO can be an order of magnitude better than the original FP-tree algorithm, even with the assumption of an optimally chosen support threshold. We also propose the mining of "N-most interesting k-itemsets with item constraints." This allows user to specify different degrees of interestingness for different itemsets. Experiments show that our proposed Double FP-trees algorithm, which is based on BOMO, is highly efficient in solving this problem.