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.     

Efficient closed high-utility pattern fusion model in large-scale databases

High-Utility Itemset Mining (HUIM) is considered a major issue in recent decades since it reveals profit strategies for use in industry for decision-making. Most existing works have focused on mining high-utility itemsets from databases showing large amount of patterns; however exact decisions are still challenging to make from that large amounts of discovered knowledge. Closed High-utility itemset mining (CHUIM) provides a smart way to present concise high-utility itemsets that can be more effective for making correct decisions. However, none of the existing works have focused on handling large-scale databases to integrate discovered knowledge from several distributed databases. In this paper, we first present a large-scale information fusion architecture to integrate discovered closed high-utility patterns from several distributed databases. The generic composite model is used to cluster transactions regarding their relevant correlation that can ensure correctness and completeness of the fusion model. The well-known MapReduce framework is then deployed in the developed DFM-Miner algorithm to handle big datasets for information fusion and integration. Experiments are then compared to the state-of-the-art CHUI-Miner and CLS-Miner algorithms for mining closed high-utility patterns and the results indicated that the designed model is well designed for handling large-scale databases with less memory usage. Moreover, the designed MapReduce framework can speed up the mining performance of closed high-utility patterns in the developed fusion system.     

Efficient incremental mining of contrast patterns in changing data

A contrast pattern is a set of items (itemset) whose frequency differs significantly between two classes of data. Such patterns describe distinguishing characteristics between datasets, are meaningful to human experts, have strong discriminating ability and can be used for powerful classifiers. Incrementally mining such patterns is very important for evolving datasets, where transactions can be either inserted or deleted and mining needs to be repeated after changes occur. When the change is small, it is undesirable to carry out mining from scratch. Rather, the set of previously mined contrast patterns should be reused where possible to compute the new patterns. A primary example of evolving data is a data stream, where the data is a sequence of continuously arriving transactions (or itemsets). In this paper, we propose an efficient technique for incrementally mining contrast patterns. Our algorithm particularly aims to avoid redundant computation which might occur due to simultaneous transaction insertion and deletion, as is the case for data streams. In an experimental study using real and synthetic data streams, we show our algorithm can be substantially faster than the previous approach.     

基于BCU-Tree与字典的高效用挖掘快速脱敏算法

针对隐私保护效用挖掘（PPUM）中脱敏时间长、计算复杂度高,以及算法副作用大等问题,提出一种基于BCU-Tree和字典（BCUTD）的高效用挖掘快速脱敏算法。该算法提出了一种新的树结构BCU-Tree来存储敏感项信息,基于按位运算符编码模型降低树的构建时间并减小搜索空间。采用字典表存储树结构中的所有节点,修改敏感项时只需访问字典表,最终达到数据库脱敏目的。在4个不同的数据集上进行的实验中,BCUTD算法在脱敏时间和副作用上的表现要明显优于经典的优先隐藏高效用项（HHUIF）算法、最大敏感效用-最大项效用（MSU-MAU）算法和使用树与表结构的快速扰动（FPUTT）算法。实验结果表明,BCUTD算法能够有效减少脱敏时间,降低算法副作用以及计算复杂度。     

一种高效的频繁模式挖掘算法

在分析研究具有代表性的关联知识挖掘算法的基础上,提出了挖掘频繁模式的一个新的数据库存储结构AFP-树,并在此结构上设计了一个频繁模式挖掘算法。理论研究已经阐明了AFP-树的有效性和相关算法的高效性。     

Efficient mining of sequential patterns with time constraints by delimited pattern growth

An active research topic in data mining is the discovery of sequential patterns, which finds all frequent subsequences in a sequence database. The generalized sequential pattern (GSP) algorithm was proposed to solve the mining of sequential patterns with time constraints, such as time gaps and sliding time windows. Recent studies indicate that the pattern-growth methodology could speed up sequence mining. However, the capabilities to mine sequential patterns with time constraints were previously available only within the Apriori framework. Therefore, we propose the DELISP (delimited sequential pattern) approach to provide the capabilities within the pattern-growth methodology. DELISP features in reducing the size of projected databases by bounded and windowed projection techniques. Bounded projection keeps only time-gap valid subsequences and windowed projection saves nonredundant subsequences satisfying the sliding time-window constraint. Furthermore, the delimited growth technique directly generates constraint-satisfactory patterns and speeds up the pattern growing process. The comprehensive experiments conducted show that DELISP has good scalability and outperforms the well-known GSP algorithm in the discovery of sequential patterns with time constraints.     

An efficient algorithm for mining frequent inter-transaction patterns

In this paper, we propose an efficient method for mining all frequent inter-transaction patterns. The method consists of two phases. First, we devise two data structures: a dat-list, which stores the item information used to find frequent inter-transaction patterns; and an ITP-tree, which stores the discovered frequent inter-transaction patterns. In the second phase, we apply an algorithm, called ITP-Miner (Inter-Transaction Patterns Miner), to mine all frequent inter-transaction patterns. By using the ITP-tree, the algorithm requires only one database scan and can localize joining, pruning, and support counting to a small number of dat-lists. The experiment results show that the ITP-Miner algorithm outperforms the FITI (First Intra Then Inter) algorithm by one order of magnitude.     

Computing the minimum-support for mining frequent patterns

Frequent pattern mining is based on the assumption that users can specify the minimum-support for mining their databases. It has been recognized that setting the minimum-support is a difficult task to users. This can hinder the widespread applications of these algorithms. In this paper we propose a computational strategy for identifying frequent itemsets, consisting of polynomial approximation and fuzzy estimation. More specifically, our algorithms (polynomial approximation and fuzzy estimation) automatically generate actual minimum-supports (appropriate to a database to be mined) according to users’ mining requirements. We experimentally examine the algorithms using different datasets, and demonstrate that our fuzzy estimation algorithm fittingly approximates actual minimum-supports from the commonly-used requirements. This work is partially supported by Australian ARC grants for discovery projects (DP0449535, DP0559536 and DP0667060), a China NSF Major Research Program (60496327), a China NSF grant (60463003), an Overseas Outstanding Talent Research Program of the Chinese Academy of Sciences (06S3011S01), and an Overseas-Returning High-level Talent Research Program of China Human-Resource Ministry. A preliminary and shortened version of this paper has been published in the Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence (PRICAI ’04).     

DBC: a condensed representation of frequent patterns for efficient mining

Given a large set of data, a common data mining problem is to extract the frequent patterns occurring in this set. The idea presented in this paper is to extract a condensed representation of the frequent patterns called disjunction-bordered condensation (DBC), instead of extracting the whole frequent pattern collection. We show that this condensed representation can be used to regenerate all frequent patterns and their exact frequencies. Moreover, this regeneration can be performed without any access to the original data. Practical experiments show that the DBCcan be extracted very efficiently even in difficult cases and that this extraction and the regeneration of the frequent patterns is much more efficient than the direct extraction of the frequent patterns themselves. We compared the DBC with another representation of frequent patterns previously investigated in the literature called frequent closed sets. In nearly all experiments we have run, the DBC have been extracted much more efficiently than frequent closed sets. In the other cases, the extraction times are very close.     

Efficient updating of discovered high-utility itemsets for transaction deletion in dynamic databases

Most algorithms related to association rule mining are designed to discover frequent itemsets from a binary database. Other factors such as profit, cost, or quantity are not concerned in binary databases. Utility mining was thus proposed to measure the utility values of purchased items for finding high-utility itemsets from a static database. In real-world applications, transactions are changed whether insertion or deletion in a dynamic database. An existing maintenance approach for handling high-utility itemsets in dynamic databases with transaction deletion must rescan the database when necessary. In this paper, an efficient algorithm, called PRE-HUI-DEL, for updating high-utility itemsets based on the pre-large concept for transaction deletion is proposed. The pre-large concept is used to partition transaction-weighted utilization itemsets into three sets with nine cases according to whether they have large (high), pre-large, or small transaction-weighted utilization in the original database and in the deleted transactions. Specific procedures are then applied to each case for maintaining and updating the discovered high-utility itemsets. Experimental results show that the proposed PRE-HUI-DEL algorithm outperforms a batch two-phase algorithm and a FUP2-based algorithm in maintaining high-utility itemsets.     

一种有效的基于图遍历的加权序列模式挖掘算法

为解决加权遍历模式挖掘问题,概括了加权有向图的种类,提出一种边加权有向图与顶点加权有向图间的变换模型,并基于该模型提出一种基于图遍历的加权序列模式挖掘算法GTWSPMiner.该算法根据遍历模式中的项的连续性特点,采用一种加权前缀投影序列模式增长方法,将原挖掘序列数据库的任务分解成一组挖掘局部投影数据库的小任务.对比实验结果表明,该算法能快速有效地挖掘加权频繁遍历模式.     

Efficient data mining for calling path patterns in GSM networks

In this paper, we explore a new data mining capability that involves mining calling path patterns in global system for mobile communication (GSM) networks. Our proposed method consists of two phases. First, we devise a data structure to convert the original calling paths in the log file into a frequent calling path graph. Second, we design an algorithm to mine the calling path patterns from the frequent calling path graph obtained. By using the frequent calling path graph to mine the calling path patterns, our proposed algorithm does not generate unnecessary candidate patterns and requires less database scans. If the corresponding calling path graph of the GSM network can be fitted in the main memory, our proposed algorithm scans the database only once. Otherwise, the cellular structure of the GSM network is divided into several partitions so that the corresponding calling path sub-graph of each partition can be fitted in the main memory. The number of database scans for this case is equal to the number of partitioned sub-graphs. Therefore, our proposed algorithm is more efficient than the PrefixSpan and a priori-like approaches. The experimental results show that our proposed algorithm outperforms the a priori-like and PrefixSpan approaches by several orders of magnitude.     

Prediction of user navigation patterns by mining the temporal web usage evolution

Advances in the data mining technologies have enabled the intelligent Web abilities in various applications by utilizing the hidden user behavior patterns discovered from the Web logs. Intelligent methods for discovering and predicting user’s patterns is important in supporting intelligent Web applications like personalized services. Although numerous studies have been done on Web usage mining, few of them consider the temporal evolution characteristic in discovering web user’s patterns. In this paper, we propose a novel data mining algorithm named Temporal N-Gram (TN-Gram) for constructing prediction models of Web user navigation by considering the temporality property in Web usage evolution. Moreover, three kinds of new measures are proposed for evaluating the temporal evolution of navigation patterns under different time periods. Through experimental evaluation on both of real-life and simulated datasets, the proposed TN-Gram model is shown to outperform other approaches like N-gram modeling in terms of prediction precision, in particular when the web user’s navigating behavior changes significantly with temporal evolution.     

A regression-based approach for mining user movement patterns from random sample data

Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users.     

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.     

Effective database transformation and efficient support computation for mining sequential patterns

In this paper, we propose a novel algorithm for mining frequent sequences from transaction databases. The transactions of the same customers form a set of customer sequences. A sequence (an ordered list of itemsets) is frequent if the number of customer sequences containing it satisfies the user-specified threshold. The 1-sequence is a special type of sequences because it consists of only a single itemset instead of an ordered list, while the k-sequence is a sequence composed of k itemsets. Compared with the cost of mining frequent k-sequences (k ≥ 2), the cost of mining frequent 1-sequences is negligible. We adopt a two-phase architecture to find the two types of frequent sequences separately in order that the discovery of frequent k-sequences can be well designed and optimized. For efficient frequent k-sequence mining, every frequent 1-sequence is encoded as a unique symbol and the database is transformed into one constituted by the symbols. We find that it is unnecessary to encode all the frequent 1-seqences, and make full use of the discovered frequent 1-sequences to transform the database into one with a smaller size. For every k ≥ 2, the customer sequences in the transformed database are scanned to find all the frequent k-sequences. We devise the compact representation for a customer sequence and elaborate the method to enumerate all distinct subsequences from a customer sequence without redundant scans. The soundness of the proposed approach is verified and a number of experiments are performed. The results show that our approach outperforms the previous works in both scalability and execution time.     

高效的用户移动模式挖掘方法

提出一种挖掘用户移动模式的新方法:利用图存储技术来挖掘用户潜在最大频繁移动模式,并实现了移动模式的增量挖掘.实验与性能分析表明,该方法比其他挖掘方法高效.     

Top-down mining of frequent closed patterns from very high dimensional data

Frequent pattern mining is an essential theme in data mining. Existing algorithms usually use a bottom-up search strategy. However, for very high dimensional data, this strategy cannot fully utilize the minimum support constraint to prune the rowset search space. In this paper, we propose a new method called top-down mining together with a novel row enumeration tree to make full use of the pruning power of the minimum support constraint. Furthermore, to efficiently check if a rowset is closed, we develop a method called the trace-based method. Based on these methods, an algorithm called TD-Close is designed for mining a complete set of frequent closed patterns. To enhance its performance further, we improve it by using new pruning strategies and new data structures that lead to a new algorithm TTD-Close. Our performance study shows that the top-down strategy is effective in cutting down search space and saving memory space, while the trace-based method facilitates the closeness-checking. As a result, the algorithm TTD-Close outperforms the bottom-up search algorithms such as Carpenter and FPclose in most cases. It also runs faster than TD-Close.     

数据挖掘在足球运动中的应用

数据挖掘是指从数据库的大量数据中揭示隐含的、先前未知的、潜在有用信息的非平凡的过程.使用可视化数据挖掘的技术从足球比赛的数据集中找到模式.这些模式可以在足球比赛中直接或间接地提供有益的见解,并在比赛中运用决策支持系统.