Applying the maximum utility measure in high utility sequential pattern mining

Recently, high utility sequential pattern mining has been an emerging popular issue due to the consideration of quantities, profits and time orders of items. The utilities of subsequences in sequences in the existing approach are difficult to be calculated due to the three kinds of utility calculations. To simplify the utility calculation, this work then presents a maximum utility measure, which is derived from the principle of traditional sequential pattern mining that the count of a subsequence in the sequence is only regarded as one. Hence, the maximum measure is properly used to simplify the utility calculation for subsequences in mining. Meanwhile, an effective upper-bound model is designed to avoid information losing in mining, and also an effective projection-based pruning strategy is designed as well to cause more accurate sequence-utility upper-bounds of subsequences. The indexing strategy is also developed to quickly find the relevant sequences for prefixes in mining, and thus unnecessary search time can be reduced. Finally, the experimental results on several datasets show the proposed approach has good performance in both pruning effectiveness and execution efficiency.     

On-shelf utility mining with negative item values

On-shelf utility mining has recently received interest in the data mining field due to its practical considerations. On-shelf utility mining considers not only profits and quantities of items in transactions but also their on-shelf time periods in stores. Profit values of items in traditional on-shelf utility mining are considered as being positive. However, in real-world applications, items may be associated with negative profit values. This paper proposes an efficient three-scan mining approach to efficiently find high on-shelf utility itemsets with negative profit values from temporal databases. In particular, an effective itemset generation method is developed to avoid generating a large number of redundant candidates and to effectively reduce the number of data scans in mining. Experimental results for several synthetic and real datasets show that the proposed approach has good performance in pruning effectiveness and execution efficiency.     

一种高效用项集并行挖掘算法

由于能反映用户的偏好,可以弥补传统频繁项集挖掘仅由支持度来衡量项集重要性的不足,高效用项集正在成为当前数据挖掘研究的热点。为使高效用项集挖掘更好地适应数据规模不断增大的实际需求,提出了一种高效用项集的并行挖掘算法PHUI-Mine。提出了记录挖掘高效用项集信息的DHUI-树结构,描述了DHUI-树的构造方法,论证了DHUI-树的动态剪枝策略。在此基础上,给出了高效用项集挖掘的并行算法描述。实验结果表明,PHUI-Mine算法具有较高的挖掘效率及较低的存储开销。     

An incremental mining algorithm for high utility itemsets

Association-rule mining, which is based on frequency values of items, is the most common topic in data mining. In real-world applications, customers may, however, buy many copies of products and each product may have different factors, such as profits and prices. Only mining frequent itemsets in binary databases is thus not suitable for some applications. Utility mining is thus presented to consider additional measures, such as profits or costs according to user preference. In the past, a two-phase mining algorithm was designed for fast discovering high utility itemsets from databases. When data come intermittently, the approach needs to process all the transactions in a batch way. In this paper, an incremental mining algorithm for efficiently mining high utility itemsets is proposed to handle the above situation. It is based on the concept of the fast-update (FUP) approach, which was originally designed for association mining. The proposed approach first partitions itemsets into four parts according to whether they are high transaction-weighted utilization itemsets in the original database and in the newly inserted transactions. Each part is then executed by its own procedure. Experimental results also show that the proposed algorithm executes faster than the two-phase batch mining algorithm in the intermittent data environment     

含负项top-k高效用项集挖掘算法

含负项高效用项集(HUI)挖掘是新兴的数据挖掘任务之一.为了挖掘满足用户需求的含负项HUI结果集,提出了含负项top-k高效用项集(THN)挖掘算法.为了提升THN算法的时空性能,提出了自动提升最小效用阈值的策略,并采用模式增长方法进行深度优先搜索;使用重新定义的子树效用和重新定义的本地效用修剪搜索空间;使用事务合并技...     

基于Spark的并行化高效用项集挖掘算法

针对传统基于链表结构的Top-K高效用挖掘算法在大数据环境下不能满足挖掘需求的问题,提出一种基于Spark的并行化高效用项集挖掘算法(STKO)。首先从阈值提升、搜索空间缩小等方面对TKO算法进行改进;然后选择Spark平台,改变原有数据存储结构,利用广播变量优化迭代过程,在避免大量重新计算的同时使用负载均衡思想实现Top-K高效用项集的并行挖掘。实验结果表明,该并行算法能有效地挖掘出大数据集中的高效用项集。     

A framework for mining interesting high utility patterns with a strong frequency affinity

High utility pattern (HUP) mining is one of the most important research issues in data mining. Although HUP mining extracts important knowledge from databases, it requires long calculations and multiple database scans. Therefore, HUP mining is often unsuitable for real-time data processing schemes such as data streams. Furthermore, many HUPs may be unimportant due to the poor correlations among the items inside of them. Hence,the fast discovery of fewer but more important HUPs would be very useful in many practical domains. In this paper, we propose a novel framework to introduce a very useful measure, called frequency affinity, among the items in a HUP and the concept of interesting HUP with a strong frequency affinity for the fast discovery of more applicable knowledge. Moreover, we propose a new tree structure, utility tree based on frequency affinity (UTFA), and a novel algorithm, high utility interesting pattern mining (HUIPM), for single-pass mining of HUIPs from a database. Our approach mines fewer but more valuable HUPs, significantly reduces the overall runtime of existing HUP mining algorithms and is applicable to real-time data processing. Extensive performance analyses show that the proposed HUIPM algorithm is very efficient and scalable for interesting HUP mining with a strong frequency affinity.     

High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates

High utility itemset mining considers the importance of items such as profit and item quantities in transactions. Recently, mining high utility itemsets has emerged as one of the most significant research issues due to a huge range of real world applications such as retail market data analysis and stock market prediction. Although many relevant algorithms have been proposed in recent years, they incur the problem of generating a large number of candidate itemsets, which degrade mining performance. In this paper, we propose an algorithm named MU-Growth (Maximum Utility Growth) with two techniques for pruning candidates effectively in mining process. Moreover, we suggest a tree structure, named MIQ-Tree (Maximum Item Quantity Tree), which captures database information with a single-pass. The proposed data structure is restructured for reducing overestimated utilities. Performance evaluation shows that MU-Growth not only decreases the number of candidates but also outperforms state-of-the-art tree-based algorithms with overestimated methods in terms of runtime with a similar memory usage.     

CLS-Miner: efficient and effective closed high-utility itemset mining

High-utility itemset mining (HUIM) is a popular data mining task with applications in numerous domains. However, traditional HUIM algorithms often produce a very large set of high-utility itemsets (HUIs). As a result, analyzing HUIs can be very time consuming for users. Moreover, a large set of HUIs also makes HUIM algorithms less efficient in terms of execution time and memory consumption. To address this problem, closed high-utility itemsets (CHUIs), concise and lossless representations of all HUIs, were proposed recently. Although mining CHUIs is useful and desirable, it remains a computationally expensive task. This is because current algorithms often generate a huge number of candidate itemsets and are unable to prune the search space effectively. In this paper, we address these issues by proposing a novel algorithm called CLS-Miner. The proposed algorithm utilizes the utility-list structure to directly compute the utilities of itemsets without producing candidates. It also introduces three novel strategies to reduce the search space, namely chain-estimated utility co-occurrence pruning, lower branch pruning, and pruning by coverage. Moreover, an effective method for checking whether an itemset is a subset of another itemset is introduced to further reduce the time required for discovering CHUIs. To evaluate the performance of the proposed algorithm and its novel strategies, extensive experiments have been conducted on six benchmark datasets having various characteristics. Results show that the proposed strategies are highly efficient and effective, that the proposed CLS-Miner algorithmoutperforms the current state-ofthe- art CHUD and CHUI-Miner algorithms, and that CLSMiner scales linearly.     

HHUIM：一种新的启发式高效用项集挖掘方法

针对基于启发式的高效用项集挖掘算法在挖掘过程中可能丢失大量项集的问题,提出一种新的启发式高效用项集挖掘算法HHUIM。HHUIM利用哈里斯鹰优化算法进行种群的更新,能够有效减少项集的丢失。提出并设计了鹰的替换策略,解决了搜索空间较大的问题,降低了适应度函数值低于最小效用阈值的鹰的数量。此外,提出存储回溯策略,可有效防止算法收敛过快达到局部最优。大量的实验表明,所提算法优于目前最先进的启发式高效用项集挖掘算法。     

基于正负效用划分的高效用模式挖掘方法综述

高效用模式挖掘（HUPM）是新兴的数据科学研究内容之一,通过考虑事务数据库中项的单位利润和数量,以提取出更有用的信息。传统的HUPM方法假定所有项的效用值均为正,但是在实际应用中,某些数据项的效用值可能为负（如商品因产生亏损而导致利润值为负）,含负项的模式挖掘与仅含正项的模式挖掘同样重要。首先,阐述了HUPM的相关概念,并分别给出相应正负效用的实例;然后,以正与负角度划分了HUPM方法,其中带有正效用的模式挖掘方法进一步以动态与静态的数据库新颖角度划分,带有负效用的模式挖掘方法中包括了基于先验、基于树、基于效用列表和基于数组等关键技术,并从不同方面对这些方法进行了讨论和总结;最后,给出了现有HUPM方法的不足和下一步研究方向。     

一种基于划分的高效用长项集挖掘算法

效用（utility）可弥补支持度在表现语义重要性方面的不足。现有的几种基于效用的关联规则挖掘算法都采用了类似Apriori自底向上的搜索方法,不适合长模式的挖掘。提出了一种双向搜索高效用项集的模型及一种基于划分的inter-transaction算法。inter-transaction利用了长事务相交迅速变短的特性和新的减枝策略,能同时输出项集的效用与支持度。实验表明,该方法对蕴含长模式的高维数据库非常有效。     

数据流高效用模式挖掘综述

数据流高效用模式挖掘方法是以二进制的频繁模式挖掘方法为前提,引入项的内部效用和外部效用,在模式挖掘过程中可以考虑项的重要性,从而挖掘更有价值的模式。从关键窗口技术、常用方法、表示形式等角度对数据流高效用模式挖掘方法进行分析并总结其相关算法,从而研究其特点、优势、劣势以及其关键问题所在。具体来说,说明了数据流高效用模式常用的概念;对处理数据流高效用模式的关键窗口技术进行了分析,涉及到滑动、衰减、界标和倾斜窗口模型;研究了一阶段和两阶段的数据流高效用模式挖掘方法;分析了高效用模式的表示形式,即完全高效用模式和压缩高效用模式;介绍了其他的数据流高效用模式,包括序列高效用模式、混合高效用模式以及高平均效用模式等;最后展望了数据流高效用模式挖掘的进一步研究方向。     

基于模糊质心的混合属性数据模糊加权聚类算法

在模糊聚类算法中,模糊系数被用来控制簇可能重叠的程度,其负面影响是所有的数据对象会影响所有的簇。为解决该问题,Klawonn和Hppner使用模糊函数替换模糊系数(KH算法),但该方法是针对数值属性数据而设计的。然而,在许多真实的应用中,数据对象通常同时由数值属性和分类属性描述。面向混合属性数据,文中提出了一种新的基于模糊质心的模糊加权聚类算法。首先结合模糊质心和均值来表示混合属性条件下的簇中心,然后使用能够评估不同属性在聚类过程中作用的度量来评估数据对象和簇中心之间的相异度,最后给出算法框架。在3个混合属性数据集上对新算法进行了一系列的测试,实验结果表明新算法的性能优于传统算法。     

An efficient algorithm for mining temporal high utility itemsets from data streams

Utility of an itemset is considered as the value of this itemset, and utility mining aims at identifying the itemsets with high utilities. The temporal high utility itemsets are the itemsets whose support is larger than a pre-specified threshold in current time window of the data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like association rules from data streams. In this paper, we propose a novel method, namely THUI (Temporal High Utility Itemsets)-Mine, for mining temporal high utility itemsets from data streams efficiently and effectively. To the best of our knowledge, this is the first work on mining temporal high utility itemsets from data streams. The novel contribution of THUI-Mine is that it can effectively identify the temporal high utility itemsets by generating fewer candidate itemsets such that the execution time can be reduced substantially in mining all high utility itemsets in data streams. In this way, the process of discovering all temporal high utility itemsets under all time windows of data streams can be achieved effectively with less memory space and execution time. This meets the critical requirements on time and space efficiency for mining data streams. Through experimental evaluation, THUI-Mine is shown to significantly outperform other existing methods like Two-Phase algorithm under various experimental conditions.     

基于R-list的Top-K高效用项集挖掘算法

针对现有的一阶段Top-K高效用项集挖掘算法挖掘过程中阈值提升慢,迭代时生成大量候选项集造成内存占用过多等问题,提出一种基于重用链表(R-list)的Top-K高效用挖掘算法RHUM。使用一种新的数据结构R-list来存储并快速访问项集信息,无需第2次扫描数据库进行项集挖掘。该算法重用内存以保存候选集信息,结合改进的RSD阈值提升策略对数据进行预处理,期间采用更严格的剪枝参数在递归搜索的过程中同时计算多个项集的效用来缩小搜索空间。在不同类型数据集中的实验结果表明:RHUM算法在内存效率方面均优于其他一阶段算法,且在K值变化时能保持稳定。     

Frequent itemset mining using cellular learning automata

A core issue of the association rule extracting process in the data mining field is to find the frequent patterns in the database of operational transactions. If these patterns discovered, the decision making process and determining strategies in organizations will be accomplished with greater precision. Frequent pattern is a pattern seen in a significant number of transactions. Due to the properties of these data models which are unlimited and high-speed production, these data could not be stored in memory and for this reason it is necessary to develop techniques that enable them to be processed online and find repetitive patterns. Several mining methods have been proposed in the literature which attempt to efficiently extract a complete or a closed set of different types of frequent patterns from a dataset. In this paper, a method underpinned upon Cellular Learning Automata (CLA) is presented for mining frequent itemsets. The proposed method is compared with Apriori, FP-Growth and BitTable methods and it is ultimately concluded that the frequent itemset mining could be achieved in less running time. The experiments are conducted on several experimental data sets with different amounts of minsup for all the algorithms as well as the presented method individually. Eventually the results prod to the effectiveness of the proposed method.     

基于模式增长方式的高效用模式挖掘算法

高效用模式挖掘是数据挖掘领域的一个重要研究内容; 由于其计算过程包含对模式的内、外效用值的处理, 计算复杂度较大, 因此挖掘算法的主要研究热点问题就是提高算法的时间效率.针对此问题, 本文给出一个基于模式增长方式的高效用模式挖掘算法HUPM-FP, 该算法可以从全局树上挖掘高效用模式, 避免产生候选项集.实验中, 采用6个典型数据集进行实验, 并和目前效率较好的算法FHM (Faster high-utility itemset mining)做了对比, 实验结果表明本文给出的算法时空效率都有较大的提高, 特别是时间效率提高较大, 可以达到1个数量级以上.     

Fuzzy data mining for time-series data

Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In the past, many approaches based on regression, neural networks and other mathematical models were proposed to analyze the time series. In this paper, we attempt to use the data mining technique to analyze time series. Many previous studies on data mining have focused on handling binary-valued data. Time series data, however, are usually quantitative values. We thus extend our previous fuzzy mining approach for handling time-series data to find linguistic association rules. The proposed approach first uses a sliding window to generate continues subsequences from a given time series and then analyzes the fuzzy itemsets from these subsequences. Appropriate post-processing is then performed to remove redundant patterns. Experiments are also made to show the performance of the proposed mining algorithm. Since the final results are represented by linguistic rules, they will be friendlier to human than quantitative representation.     

Mining Maximal High Utility Itemsets on Dynamic Profit Databases

Abstract

To overcome the limitation of high-utility itemset mining, more compact, lossless, and concise representations of high utility itemsets (HUIs) have been proposed in previous works, such as closed HUIs (CHUIs) or maximal HUIs (MHUIs). Focusing into MHUI mining, in this article, we present efficient approaches to directly mine MHUIs from transactional databases without generating any candidates. The proposed algorithms, which all execute in one phase, utilize many efficient data structures and pruning techniques such as EUCP combined with EUCS, CUIP combined with FUCS, and the P-set structure to significantly reduce the search space and remove nonpromising itemsets, thus, increase the performance of the MHUI mining process. Furthermore, while previous works assumed that the unit profit of items is fixed, which is not practical in many real-world applications, our work resolved this issue by applying a new utility calculation into the mining process to reflect the true nature of real-world databases, thus, generating more accurate results.