Mining Frequent Sequential Rules with An Efficient Parallel Algorithm

Sequential rule mining is one of the most common data mining techniques. It intends to find desired rules in large sequence databases. It can decide the essential information that helps acquire knowledge from large search spaces and select curiously rules from sequence databases. The key challenge is to avoid wasting time, which is particularly difficult in large sequence databases. This paper studies the mining rules from two representations of sequential patterns to have compact databases without affecting the final result. In addition, execute a parallel approach by utilizing multi core processor architecture for mining non-redundant sequential rules. Also, perform pruning techniques to enhance the efficiency of the generated rules. The evaluation of the proposed algorithm was accomplished by comparing it with another non-redundant sequential rule algorithm called Non-Redundant with Dynamic Bit Vector (NRD-DBV). Both algorithms were performed on four real datasets with different characteristics. Our experiments show the performance of the proposed algorithm in terms of execution time and computational cost. It achieves the highest efficiency, especially for large datasets and with low values of minimum support, as it takes approximately half the time consumed by the compared algorithm.

A Study of the Pupal Development of Five Forensically Important Flies (Diptera: Brachycera)

Holometabolous insects undergo complete metamorphosis, and hence, they have different phases of development (egg, larva, pupa, and adult), which occupy distinct ecological niches. The pupae of several fly species are surrounded by the puparium, which is a rigid structure, usually formed by the integument of the last larval instar. The puparium presents unique characteristics distinct from those of the larval and adult phases. During intrapuparial development, it is possible to distinguish at least four fundamental and continuous steps, namely: 1) larval–pupal apolysis, 2) cryptocephalic pupa, 3) phanerocephalic pupa, and 4) pharate adult. The objective of this work was to describe the external morphology of the distinct phase of development for five species that were collected, identified, and raised in the laboratory; intrapuparial development was studied by fixing immature specimens at regular intervals; the morphological analyses were performed with the aid of both light and scanning electron microscopy. Under the conditions established (27 ± 1.0 or 23 ± 1.0°C, 60 ± 10% relative humidity, 12 h of photoperiod), the minimum time for intrapuparial development was: 252 h for Megaselia scalaris (Loew 1966) (Phoridae), 192 h for Piophila casei (Linnaeus 1758) (Piophilidae), Fannia pusio (Wiedemann 1830) (Fanniidae), and Musca domestica (Linnaeus 1758) (Muscidae), and 96 h for Chrysomya megacephala (Fabricius 1794) (Calliphoridae). Intrapuparial development has defined steps, and distinct species responded differently to the same environmental conditions. In addition, it is possible to establish a sequential rule without ignoring the specific characteristics of each taxon.

Two-dimensional reward evaluation in mice

When choosing among multi-attribute options, integrating the full information may be computationally costly and time-consuming. So-called non-compensatory decision rules only rely on partial information, for example when a difference on a single attribute overrides all others. Such rules may be ecologically more advantageous, despite being economically suboptimal. Here, we present a study that investigates to what extent animals rely on integrative rules (using the full information) versus non-compensatory rules when choosing where to forage. Groups of mice were trained to obtain water from dispensers varying along two reward dimensions: volume and probability. The mice’s choices over the course of the experiment suggested an initial reliance on integrative rules, later displaced by a sequential rule, in which volume was evaluated before probability. Our results also demonstrate that while the evaluation of probability differences may depend on the reward volumes, the evaluation of volume differences is seemingly unaffected by the reward probabilities.

In-queue priority purchase: a dynamic game approach

Pay-for-priority is a common practice in congestion-prone service systems. The extant literature on this topic restricts attention to the case where the only epoch for customers to purchase priority is upon arrival, and if customers choose not to upgrade when they arrive, they cannot do so later during their wait. A natural alternative is to let customers pay and upgrade to priority at any time during their stay in the queue, even if they choose not to do so initially. This paper builds a queueing-game-theoretic model that explicitly captures self-interested customers’ dynamic in-queue priority-purchasing behavior. When all customers (who have not upgraded yet) simultaneously decide whether to upgrade, we find in our model that pure-strategy equilibria do not exist under some intuitive criteria, contrasting the findings in classical models where customers can only purchase priority upon arrival. However, when customers sequentially decide whether to upgrade, threshold-type pure-strategy equilibria may exist. In particular, under sufficiently light traffic, if the number of ordinary customers accumulates to a certain threshold, then it is always the second last customer who upgrades, but in general, it could be a customer from another position, and the queue-length threshold that triggers an upgrade can also vary with the traffic intensity. Finally, we find that in-queue priority purchase subject to the sequential rule yields less revenue than upon-arrival priority purchase in systems with small buffers.

A System for Predictive Data Analytics Using Sequential Rule Mining

In the current scenario of the business world, the importance of data analytics is quite large. It certainly benefits the businesses in the decision-making process. Sequential rule mining can be widely utilized to extract important data having variety of applications like e-commerce, stock market analysis, etc. Predictive data analytics using the sequential rule mining consists of analyzing input sequences and finding sequential rules that can help businesses in decision making. This article presents an approach called M_TRuleGrowth that generates partially-ordered sequential rules efficiently. The authors conducted an experimental evaluation on real world dataset that provides strong evidence that M_TRuleGrowth performs better in terms of execution time.

Segmentation-Based Sequential Rules For Product Promotion Recommendations As Sales Strategy (Case Study: Dayra Store)

One of the problems in the promotion is the high cost. Identifying the customer segments that have made transactions, sellers can promote better products to potential consumers. The segmentation of potential consumers can be integrated with the products that consumers tend to buy. The relationship can be found using pattern analysis using the Association Rule Mining (ARM) method. ARM will generate rule patterns from the old transaction data, and the rules can be used for recommendations. This study uses a segmented-based sequential rule method that generates sequential rules from each customer segment to become product promotion for potential consumers. The method was tested by comparing product promotions based on rules and product promotions without based on rules. Based on the test results, the average percentage of transaction from product promotion based on rules is 2,622%, higher than the promotion with the latest products with an average rate of transactions only 0,315%. The hypothesis in each segment obtained from the sample can support the statement that product promotion in all segments based on rules can be more effective in increasing sales compared to promotions that use the latest products without using rules recommendations.

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

As an important technology in computer science, data mining aims to mine hidden, previously unknown, and potentially valuable patterns from databases.High utility negative sequential rule (HUNSR) mining can provide more comprehensive decision-making information than high utility sequential rule (HUSR) mining by taking non-occurring events into account. HUNSR mining is much more difficult than HUSR mining because of two key intrinsic complexities. One is how to define the HUNSR mining problem and the other is how to calculate the antecedent’s local utility value in a HUNSR, a key issue in calculating the utility-confidence of the HUNSR. To address the intrinsic complexities, we propose a comprehensive algorithm called e-HUNSR and the contributions are as follows. (1) We formalize the problem of HUNSR mining by proposing a series of concepts. (2) We propose a novel data structure to store the related information of HUNSR candidate (HUNSRC) and a method to efficiently calculate the local utility value and utility of HUNSRC’s antecedent. (3) We propose an efficient method to generate HUNSRC based on high utility negative sequential pattern (HUNSP) and a pruning strategy to prune meaningless HUNSRC. To the best of our knowledge, e-HUNSR is the first algorithm to efficiently mine HUNSR. The experimental results on two real-life and 12 synthetic datasets show that e-HUNSR is very efficient.