FHUQI-Miner: Fast high utility quantitative itemset mining

Applied Intelligence - High utility itemset mining is a popular pattern mining task, which aims at revealing all sets of items that yield a high profit in a transaction database. Although this task...     

Improving the Behavior of Intelligent Tutoring Agents with Data Mining

This article presents a novel framework for adapting the behavior of intelligent agents. The framework consists of an extended sequential pattern mining algorithm that, in combination with association rule discovery techniques, is used to extract temporal patterns and relationships from the behavior of human agents executing a procedural task. The proposed framework has been integrated within the CanadarmTutor, an intelligent tutoring agent aimed at helping students solve procedural problems that involve moving a robotic arm in a complex virtual environment. We present the results of an evaluation that demonstrates the benefits of this integration to agents acting in ill-defined domains.     

Using artificial intelligence techniques for COVID-19 genome analysis

Applied Intelligence - The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province,...     

Self-adaptive nonoverlapping sequential pattern mining

Applied Intelligence - Repetitive sequential pattern mining (SPM) with gap constraints is a data analysis task that consists of identifying patterns (subsequences) appearing many times in a...     

FCloSM,FGenSM: two efficient algorithms for mining frequent closed and generator sequences using the local pruning strategy

Mining frequent sequences in sequential databases are highly valuable for many real-life applications. However, in several cases, especially when databases are huge and when low minimum support thresholds are used, the cardinality of the result set can be enormous. Consequently, algorithms for discovering frequent sequences exhibit poor performance, showing an important increase in execution time, memory consumption and storage space usage. To address this issue, researchers have studied the tasks of mining frequent closed and generator sequences, as they provide several benefits when compared to the set of frequent sequences. One of the most important benefits is that the cardinalities of frequent closed and generator sequences are generally much less than the cardinality of frequent sequences. Hence, humans find it more convenient to analyze the information provided by closed and generator sequences. Moreover, it was shown that frequent closed sequences have the advantage of being lossless, and they thus preserve information about the frequency of all frequent subsequences, while generator sequences can provide higher accuracy for sequence classification tasks since they are the smallest patterns that characterize groups of sequences. Besides, frequent closed sequences can be combined with generators to produce non-redundant sequential rules and recover the complete set of frequent sequences and their frequencies. This paper proposes two novel algorithms named FCloSM and FGenSM to mine frequent closed and generator sequences efficiently. These algorithms are based on new pruning conditions called extended early elimination (3E) and early pruning techniques named EPCLO and EPGEN, designed to identify non-closed and non-generator patterns early. Based on these techniques, two local pruning strategies called LPCLO and LPGEN are proposed to eliminate non-closed and non-generator patterns more efficiently at two successive levels of the prefix search tree without performing subsequence relation checking. These theoretical results, which are the basis of FCloSM and FGenSM, are mathematically proved and are shown to be more general than those presented in previous work. Extensive experiments show that FCloSM and FGenSM are one to two orders of magnitude faster than the state-of-the-art algorithms for discovering frequent closed sequences (CloSpan, BIDE, ClaSP and CM-ClaSP) and for mining frequent generators (FEAT, FSGP and VGEN), and that FCloSM and FGenSM consume much less memory.     

Learning task models in ill-defined domain using an hybrid knowledge discovery framework

Domain experts should provide Intelligent Tutoring Systems (ITS) with relevant domain knowledge that enable it to guide the learner during problem-solving learning activities. However, for ill-defined domains this knowledge is hard to define explicitly. Our hypothesis is that knowledge discovery (KD) techniques can be used to extract problem-solving task models from the recorded usage of expert, intermediate and novice learners. This paper proposes a procedural-knowledge acquisition framework based on a combination of sequential pattern mining and association rules discovery techniques. The framework has been implemented and is used to discover new meta-knowledge and rules in a given domain which then extend domain knowledge and serve as problem space, allowing the Intelligent Tutoring System to guide learners in problem-solving situations. Preliminary experiments have been conducted using the framework as an alternative to a path-planning problem solver in CanadarmTutor.     

FDHUP: Fast algorithm for mining discriminative high utility patterns

Recently, high utility pattern mining (HUPM) has been extensively studied. Many approaches for HUPM have been proposed in recent years, but most of them aim at mining HUPs without any consideration for their frequency. This has the major drawback that any combination of a low utility item with a very high utility pattern is regarded as a HUP, even if this combination has low affinity and contains items that rarely co-occur. Thus, frequency should be a key criterion to select HUPs. To address this issue, and derive high utility interesting patterns (HUIPs) with strong frequency affinity, the HUIPM algorithm was proposed. However, it recursively constructs a series of conditional trees to produce candidates and then derive the HUIPs. This procedure is time-consuming and may lead to a combinatorial explosion when the minimum utility threshold is set relatively low. In this paper, an efficient algorithm named fast algorithm for mining discriminative high utility patterns (DHUPs) with strong frequency affinity (FDHUP) is proposed to efficiently discover DHUPs by considering both the utility and frequency affinity constraints. Two compact structures named EI-table and FU-tree and three pruning strategies are introduced in the proposed algorithm to reduce the search space, and efficiently and effectively discover DHUPs. An extensive experimental study shows that the proposed FDHUP algorithm considerably outperforms the state-of-the-art HUIPM algorithm in terms of execution time, memory consumption, and scalability.     

Inferring social network user profiles using a partial social graph

Classification problems refer to the assignment of alternatives to predefined categories. In this work we focus on ordered classification, called sorting, in which the predefined categories indicate several degrees of interest or suitability of alternatives for a certain user. The assignment of alternatives is based on multiple conflicting criteria. This multi-criteria sorting approach is specially interesting for recommender systems aimed at finding the most suitable alternatives for each user. First, we study the ELECTRE-TRI-B sorting method, which follows the outranking approach based on comparing the evaluations of alternatives with the profile limits separating the categories. The complexity of some recommenders systems requires the extension of the classical ELECTRE-TRI-B method to manage a taxonomical organization of the set of criteria. In this paper we consider a set of criteria in the form of a hierarchy. The intermediate criteria in such a hierarchy correspond to different aspects of the recommendation procedure, such as content, context or cost. At each of these criteria, a sorting problem must be solved. Therefore, we propose extending ELECTRE-TRI-B to handle assignments of alternatives on several levels of the hierarchy. A hierarchical procedure for sorting is proposed, called ELECTRE-TRI-B-H. Secondly, the paper explains the integration of ELECTRE-TRI-B-H into a recommender system of touristic activities related to wine, called GoEno-Tur. This system is developed for the region of Tarragona, Catalonia (Spain), which is a well-recognized area of wine and cava production.     

基于统计感知策略的高斯混合模型求解方法

高斯混合模型（Gaussian mixture model,GMM）是一种经典的概率模型,常被用于无监督学习领域来确定无类别标记样本点的类别分布。作为求解GMM参数的重要技术,期望最大化（Expectation maximization,EM）算法通过计算GMM对应似然函数的最优解确定基模型自身参数以及基模型的混合系数。利用EM算法求解GMM存在如下两个缺陷：EM算法易于陷入局部最优解以及EM算法确定GMM基模型相关参数的不稳定,尤其是针对多维随机变量。本文提出了一种基于统计感知（Statistical-aware,SA）策略的GMM求解方法——SA-GMM方法。该方法从估计给定数据集的未知概率密度函数入手,建立了核密度估计（Kernel density estimation,KDE）与GMM之间的关联。为避免KDE对“过平滑”窗口的选取,设计了同时最小化KDE与GMM之间的经验风险和KDE窗口结构风险的目标函数,进而确定了GMM的最优参数。在11个标准概率分布上的实验证明了SA-GMM方法的可行性、合理性和有效性,同时结果也表明SA-GMM能够获得显著优于基于EM算法的GMM及其变体的概率密度函数估计表现。     

CMRules: Mining sequential rules common to several sequences

Sequential rule mining is an important data mining task used in a wide range of applications. However, current algorithms for discovering sequential rules common to several sequences use very restrictive definitions of sequential rules, which make them unable to recognize that similar rules can describe a same phenomenon. This can have many undesirable effects such as (1) similar rules that are rated differently, (2) rules that are not found because they are considered uninteresting when taken individually, (3) and rules that are too specific, which makes them less likely to be used for making predictions. In this paper, we address these problems by proposing a more general form of sequential rules such that items in the antecedent and in the consequent of each rule are unordered. We propose an algorithm named CMRules for mining this form of rules. The algorithm proceeds by first finding association rules to prune the search space for items that occur jointly in many sequences. Then it eliminates association rules that do not meet the minimum confidence and support thresholds according to the sequential ordering. We evaluate the performance of CMRules in three different ways. First, we provide an analysis of its time complexity. Second, we compare its performance (in terms of execution time, memory usage and scalability) with an adaptation of an algorithm from the literature that we name CMDeo. For this comparison, we use three real-life public datasets, which have different characteristics and represent three kinds of data. In many cases, results show that CMRules is faster and has a better scalability for low support thresholds than CMDeo. Lastly, we report a successful application of the algorithm in a tutoring agent.