Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.

A New Algorithm for Mining Frequent Pattern

Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidate set generation and test approach. However, candidate set generation is very costly. Han J. proposed a novel algorithm FP growth that could generate frequent pattern without candidate set. Based on the analysis of the algorithm FP growth, this paper proposes a concept of equivalent FP tree and proposes an improved algorithm, denoted as FP growth * , which is much faster in speed, and easy to realize. FP growth * adopts a modified structure of FP tree and header table, and only generates a header table in each recursive operation and projects the tree to the original FP tree. The two algorithms get the same frequent pattern set in the same transaction database, but the performance study on computer shows that the speed of the improved algorithm, FP growth * , is at least two times as fast as that of FP growth.

A Novel Incremental Mining Algorithm of Frequent Patterns for Web Usage Mining

Because data warehouse is frequently changing, incremental data leads to old knowledge which is mined formerly unavailable. In order to maintain the discovered knowledge and patterns dynamically, this study presents a novel algorithm updating for global frequent patterns-IPARUC. A rapid clustering method is introduced to divide database into n parts in IPARUC firstly, where the data are similar in the same part. Then, the nodes in the tree are adjusted dynamically in inserting process by ＂pruning and laying back＂ to keep the frequency descending order so that they can be shared to approaching optimization. Finally local frequent itemsets mined from each local dataset are merged into global frequent itemsets. The results of experimental study are very encouraging. It is obvious from experiment that IPARUC is more effective and efficient than other two contrastive methods. Furthermore, there is significant application potential to a prototype of Web log Analyzer in web usage mining that can help us to discover useful knowledge effectively, even help managers making decision.

SHAPE-BASED TIME SERIES SIMILARITY MEASURE AND PATTERN DISCOVERY ALGORITHM

Pattern discovery from time series is of fundamental importance. Most of the algorithms of pattern discovery in time series capture the values of time series based on some kinds of similarity measures. Affected by the scale and baseline, value-based methods bring about problem when the objective is to capture the shape. Thus, a similarity measure based on shape, Sh measure, is originally proposed, andthe properties of this similarity and corresponding proofs are given. Then a time series shape pattern discovery algorithm based on Sh measure is put forward. The proposed algorithm is terminated in finite iteration with given computational and storage complexity. Finally the experiments on synthetic datasets and sunspot datasets demonstrate that the time series shape pattern algorithm is valid.

城市休闲产业聚类模式APM算法模型开发与校验

城市休闲相关产业的高质量发展对当前我国城市消费升级以及人居环境质量提升具有重要现实意义。但是,现有研究未能精准地捕捉海量广域分布的城市休闲产业的基本空间分布规律与结构,而已有的空间聚类算法较多适用于城市用地分析,未能很好地适用于离散分布的城市休闲产业研究。为此,文章基于空间兴趣点数据,开发距离通达值及空间集群中心点等算法,构建城市休闲旅游产业聚类模式空间算法模型(APM)。在以广州为例的研究中,APM模型捕捉出3170个以500 m步行生活圈为范围的城市休闲产业集群,校验了APM模型的科学性与应用价值。整体上,APM算法可以较好地捕捉城市休闲业态集群的空间结构,清晰识别城市休闲产业空间冷、热点分布的基本结构,由其捕捉行程的聚类边界与实际道路和建筑走向、水系边界、区域范围等重合度高,聚类集群符合实际情况,具备可信度与有效性。该研究是休闲产业集聚机制研究的一次方法创新,在算法精度、实际应用、可视化效率上均做出了创新性推进。与Fishnet方法相比,可以更科学精准地识别城市内部多个休闲消费商圈的边界,实现了高效率的城市休闲产业集群捕捉;与同位模型相比,可以呈现多类别的城市休闲业态结构,突破了现有研究只能捕捉两类业态组团的局限。

DBMiner数据挖掘平台及其应用

数据挖掘是智能信息处理的重要工具。介绍ＤＢＭｉｎｅｒ数据挖掘平台的组成和原理，提出了多重知识模型的分层数据挖掘方法。基于综合衡量准则的决策树生成算法改善了生成决策树的结构和分类精度，基于遗传算法的ｎ维空间优化参数的搜索算法。该数据挖掘平台已应用于多个应用领域均取得满意结果，介绍了在多元合金材料预报和家庭远程医疗监护方面的应用。

一次性条件下的三支序列模式挖掘

一次性条件下的序列模式挖掘旨在从序列数据中挖掘出带有间隙约束的重复序列模式。然而,现有方法不考虑用户的兴趣度,将序列中的每个字符视作同等重要,导致许多用户不感兴趣的冗余模式被发现。为了解决这个问题,将三支决策思想引入序列模式挖掘领域,提出了一次性条件下的三支序列模式挖掘问题及其求解算法。在支持度计算方面,该算法基于深度优先搜索和回溯的策略,结合三支模式的特点以高效求解模式支持度。在候选模式生成方面,该算法采用模式连接策略缩减候选模式数量。此外,该算法还采用了并行化方案充分利用现代处理器的多核性能,提高算法的挖掘效率。最后,实验结果验证了研究一次性条件下的三支序列模式挖掘问题的意义和算法的高效性。

基于FP-Growth算法的运毒嫌疑车辆智能推荐研究

毒品运输是毒品犯罪的重要环节,虽然毒品运输的手段越来越多样化,但公路运输仍然是主要的运输方式之一,而运毒人员有着各自经典的运毒模式。文中对运毒模式进行特征挖掘,发现存在前后车伴随的规律,根据实际业务中前后车行为以半小时为时间间隔导向,建模时选择PostgreSQL数据库。在数据库中建立过往车辆前半小时中间表、后半小时中间表、中间跨度表,运用人工智能数据挖掘技术实现从大量的通行车辆中抽取车辆伴随信息,采用FP-Growth算法挖掘频繁项集,查找高频出现车牌号,通过设定阈值并找到对应的关联规则,经过缉毒民警提供的黑名单进行过滤并排序,最后进行车辆嫌疑度的推荐,为民警拦截嫌疑车辆提供支持,能够在一定程度上提高对嫌疑车辆排查的针对性、准确性和有效性。

基于数据挖掘的智能电网需求预测

随着电力需求的日益增加,获得能源效率也变得越来越重要。因此,开发准确的需求预测方法对于通过有效的系统运行确保能源效率至关重要。为此提出一种基于数据挖掘技术的需求预测方法。该方法是k-均值聚类、贝叶斯分类和ARIMA的结合。以前的大多数研究都试图从供应侧管理的角度来解决这一问题,但提出的预测模型适用于消费侧。实验中,对某市的实际荷载剖面进行案例研究。结果表明,提出的方法的最小错误率接近0.3,可以满足实际需求。

改进的Pattern Matrix算法在图书管理中的应用

根据用户的信息和图书借阅所产生的数据,分析用户的需求,利用改进的Pattern Matrix算法,从中挖掘出用户数据间的关联性,自动判断用户可能的借阅需求,从而将相关的图书信息推送给用户,增强图书管理的主动服务功能。

大图挖掘中一种基于云计算的改进SpiderMine算法

现有的图挖掘算法在云环境下难以有效地进行大规模图形的高频模式挖掘。为此,对Spider Mine算法做了改进,提出一种基于云的Spider Mine算法(c-Spider Mine)。首先,利用最小切割算法将大规模图形数据分为多个子图,使分区/融合成本最小,然后,利用Spider Mine进行模式挖掘,显著降低了大型模式生成时的组合复杂度。最后,采用一种模式键函数来保存模式,以保证所有模式可被成功恢复和融合。基于3种真实数据集的仿真实验结果表明,c-Spider Mine可高效挖掘云环境下的前K个大型模式,在不同数据规模和最小支持设置条件下,c-Spider Mine在内存使用和运行时间方面的性能均优于Spider Mine。

Research and Application on Web Information Retrieval Based on Improved FP-Growth Algorithm

A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each node are kept. Route information of different nodes in a same item are compressed into aggregative chains so that the frequent patterns will be produced in aggregative chains without generating node links and conditional pattern bases. An example of Web key words retrieval is given to analyze and verify the frequent pattern algorithm in this paper.

MNWAP-mine:一种改进的频繁模式挖掘算法

Web访问序列模式挖掘可以发现用户与网站交互的频繁模式,进而预测未来的访问模式.针对传统WAP-mine算法的不足,文中提出一种新的MNWAP-mine算法.首先,对WAP-tree的数据结构进行改进,采用一种基于Hash表的辅助存储结构辅助序列的查找,使该算法节省了序列的查找时间;其次,针对WAP-mine算法在构造WAP-tree时需两次数据库完整扫描的缺点,利用第1次扫描的结果,获得只包含频繁项集的序列,在第2遍扫描数据库时仅仅扫描包含频繁项集的序列,节省了扫描时间;最后,由于WAP-mine算法会产生大量子树,而新算法采用合并频繁子节点的方法,因此,提高了挖掘频繁模式的效率.实验结果表明:与现有的算法相比,新算法具有较高的效率和性能.

Fast Discovering Frequent Patterns for Incremental XML Queries

It is nontrivial to maintain such discovered frequent query patterns in real XML-DBMS because the transaction database of queries may allow frequent updates and such updates may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. In this paper, two incremental updating algorithms, FUX-QMiner and FUXQMiner, are proposed for efficient maintenance of discovered frequent query patterns and generation the new frequent query patterns when new XMI, queries are added into the database. Experimental results from our implementation show that the proposed algorithms have good performance. Key words XML - frequent query pattern - incremental algorithm - data mining CLC number TP 311 Foudation item: Supported by the Youthful Foundation for Scientific Research of University of Shanghai for Science and TechnologyBiography: PENG Dun-lu (1974-), male, Associate professor, Ph.D, research direction: data mining, Web service and its application, peerto-peer computing.

Application of Data Mining Method to Improve the Accuracy of Springback Prediction in Sheet Metal Forming

A new method was worked out to improve the precision of springback prediction in sheet metal forming by combining the finite element method (FEM) with the data mining (DM) technique. First the genetic algorithm (GA) was adopted for recognizing the material parameters. Then according to the even design idea, the suitable calculation scheme was confirmed, and FEM was used for calculating the springback. The computation results were compared with experiment data, the difference between them was taken as source data, and a new pattern recognition method of DM called hierarchical optimal map recognition method (HOMR) is applied for summarizing the calculation regulation in FEM. At the end, the mathematics model of the springback simulation was established. Based on the model, the calculation errors of springback can be controlled within 10% compared with the experimental results.

基于Spark的并行频繁项集挖掘算法

针对大数据环境下基于Spark的频繁模式增长(FP-Growth)算法存在创建条件频繁模式树(FP-tree)时空效率低,节点间通信开销大,以及冗余搜索等问题,提出了基于Spark的并行频繁项集挖掘算法(PAFMFI-Spark)。首先,该算法提出非负矩阵分解策略(SNMF),通过提供支持度计数查询和分解储存支持度计数的矩阵,解决了创建条件FP-tree的时空效率低的问题;其次,提出基于遗传算法的分组策略(GS-GA),均衡分配频繁1项集至各节点,解决了节点间的通信开销大的问题;最后,提出高效缩减树结构策略(ERTSS),缩减FP-tree树结构,解决了冗余搜索的问题。实验结果验证了PAFMFI-Spark算法的可行性以及相较于其他挖掘算法的性能优势,所提算法能有效适应各种数据的频繁项集挖掘。

基于数据挖掘的冷库开门行为模式分析与能耗预测

本文以某冷库运行数据为支撑,对海量数据进行了预处理,获取了冷库在不同时间段内的单次开门持续时长、上中下旬库门总开启时长、总开启次数以及冷风机开启情况、冷冻库内温度等数据,并对这些数据进行探索性分析发现其中的规律,明确了冷库开门的行为特征以及关联温度的变化情况。在这一基础上,利用数据挖掘技术提取了冷库开门行为的典型模式,以及各种开门行为模式与库内温度的关系。最后,采用MLP神经算法模型构建了冷库能耗预测模型,为冷库能耗的准确预测提供了支持。

基于改进序列模式挖掘算法的告警关联模型

在电力故障发生时,会产生大量的电力故障告警信息数据,如何从电力故障告警信息中挖掘出可靠的关联规则,对后续电力的调度运维有着重要的影响。广义序列模式(Generalized Sequential Pattern,GSP)算法通过增加时间上的约束条件提高算法的效率,适合应用于电力故障告警信息挖掘的场景。针对GSP算法中的关键参数多和不同的参数组合影响算法的准确性和可靠性的问题,将遗传算法与GSP算法相结合,自适应地得到一组较好的参数,将参数代入GSP算法,从而得到更加可靠的关联规则,以此来解决在电力故障告警信息应用中很难为不同的数据集找到合适的参数组合的问题。通过实例验证,电力故障告警信息数据应用遗传算法结合GSP算法能够有效地得到更加准确和可靠的计算结果。

一种面向高校学生体测数据的模式挖掘方法

目前,每年测试高校大学生的体质健康,会产生大量的数据,为了提高关联规则对体测数据的挖掘效率,提出了一种面向高校学生体质测试数据的模式挖掘方法。利用经典的关联规则挖掘方法如Apriori算法和频繁模式树(Frequent Pattern Tree,FP-Growth)算法,对体测数据进行关联规则挖掘。实验结果表明,该模式挖掘的最小数据集能有效提高关联规则算法对体测数据的模式挖掘效率。