An efficient algorithm for mining top-rank-k frequent patterns

Mining top-rank-k frequent patterns is a popular data mining task, which consists of discovering the patterns in a transaction database that belong to the k first ranks in terms of support. Although, several algorithms have been proposed for this task, it remains computationally expensive. To address this issue, this paper proposes a novel algorithm named BTK. It relies on a novel tree structure named TB-tree to store crucial information about frequent patterns. Moreover, BTK employs a new B-list structure to store information about patterns, and relies on subsume indexes to reduce the search space and speed up the discovery of top-rank-k frequent patterns. BTK also uses an early pruning strategy and an effective threshold raising mechanism. Additionally, BTK introduces two efficient procedures for respectively generating subsume indexes and intersecting B-lists. Extensive experiments were conducted on several datasets to evaluate the efficiency of the proposed algorithm. Results show that BTK is highly efficient and competitive.     

Accurate and precise optical testing with a differential Hartmann wavefront sensor

A novel differential Hartmann sensor is described. It can be used to determine the characteristics of an optic accurately, precisely, and simply without detailed knowledge of the wavefront used to illuminate the optical system or of the geometry of the measurement system. We demonstrate the application of this sensor to both zonal and modal optical testing of lenses. We also describe a dual-camera implementation of the sensor that would enable high-speed optical testing.     

A MICROPROCESSOR BASED CONTROL OF DC MOTOR DRIVES USING PROPORTIONAL-INTEGRAL (P-I) AND INTEGRAL-PROPORTIONAL (I-P) CONTROLLERS

ABSTRACT

This paper presents a Microprocessor based Digital control system for a DC Motor Drive using the commonly used P-I (Proportional-Integral) controller and a relatively new I-P (Integral-Proportional) controller. The relative merits and demerits of both P-I and I-P controllers are evaluated and compared. Important aspects, such as, the starting speed and current response; responses to step changes in the speed reference and the load torque; error signal processing; gain sensitivity, etc. are analyzed. It is shown that the I-P control scheme offers some distinctive advantages over the P-I control scheme. Some experimental and simulation results are also presented.     

海神之矛——美国海军特种作战研究大队队员

<正>海神之矛行动(Operation Neptune Spear)是美国于2011开展的一项针对本·拉登的军事任务。"9·11"之后,尽管美国政府推翻了阿富汗的塔利班政权,并重创了"基地"组织,但"基地"组织的核心人物本·拉登仍然不知去向,作为恐怖主义的一个代表人物,美国一直将逮捕或者铲除本·拉登作为其反恐行动的一个象征,也正因如此,美军从未停止对包括本·拉登在内的"基地"组织高层人员的打击。     

CONFINING PRESSURE EFFECT ONENGINEERING PROPERTIES OF WEAK LAYERS IN DAM SITES

1 INTRODUCTION The physical and mechanical properties of weaklayers are worse than those of rock masses[1,2]. Weaklayers,as one of the important boundaries,oftencontrol the stability and deformation of engineeringrock masses[3,4]. Therefore,the reasonable assessment ofengineering properties for weak layers is one of themost important problems in rock mass projects.The engineering properties of weak layers not onlyembody in their substances , but also reflect theinteraction between their s…     

Efficient high utility itemset mining using buffered utility-lists

Discovering high utility itemsets in transaction databases is a key task for studying the behavior of customers. It consists of finding groups of items bought together that yield a high profit. Several algorithms have been proposed to mine high utility itemsets using various approaches and more or less complex data structures. Among existing algorithms, one-phase algorithms employing the utility-list structure have shown to be the most efficient. In recent years, the simplicity of the utility-list structure has led to the development of numerous utility-list based algorithms for various tasks related to utility mining. However, a major limitation of utility-list based algorithms is that creating and maintaining utility-lists are time consuming and can consume a huge amount of memory. The reasons are that numerous utility lists are built and that the utility-list intersection/join operation to construct a utility-list is costly. This paper addresses this issue by proposing an improved utility-list structure called utility-list buffer to reduce the memory consumption and speed up the join operation. This structure is integrated into a novel algorithm named ULB-Miner (Utility-List Buffer for high utility itemset Miner), which introduces several new ideas to more efficiently discover high utility itemsets. ULB-Miner uses the designed utility-list buffer structure to efficiently store and retrieve utility-lists, and reuse memory during the mining process. Moreover, the paper also introduces a linear time method for constructing utility-list segments in a utility-list buffer. An extensive experimental study on various datasets shows that the proposed algorithm relying on the novel utility-list buffer structure is highly efficient in terms of both execution time and memory consumption. The ULB-Miner algorithm is up to 10 times faster than the FHM and HUI-Miner algorithms and consumes up to 6 times less memory. Moreover, it performs well on both dense and sparse datasets.     

An efficient algorithm for mining top-<Emphasis Type="Italic">k</Emphasis> on-shelf high utility itemsets

High on-shelf utility itemset (HOU) mining is an emerging data mining task which consists of discovering sets of items generating a high profit in transaction databases. The task of HOU mining is more difficult than traditional high utility itemset (HUI) mining, because it also considers the shelf time of items, and items having negative unit profits. HOU mining can be used to discover more useful and interesting patterns in real-life applications than traditional HUI mining. Several algorithms have been proposed for this task. However, a major drawback of these algorithms is that it is difficult for users to find a suitable value for the minimum utility threshold parameter. If the threshold is set too high, not enough patterns are found. And if the threshold is set too low, too many patterns will be found and the algorithm may use an excessive amount of time and memory. To address this issue, we propose to address the problem of top-k on-shelf high utility itemset mining, where the user directly specifies k, the desired number of patterns to be output instead of specifying a minimum utility threshold value. An efficient algorithm named KOSHU (fast top-K on-shelf high utility itemset miner) is proposed to mine the top-k HOUs efficiently, while considering on-shelf time periods of items, and items having positive and/or negative unit profits. KOSHU introduces three novel strategies, named efficient estimated co-occurrence maximum period rate pruning, period utility pruning and concurrence existing of a pair 2-itemset pruning to reduce the search space. KOSHU also incorporates several novel optimizations and a faster method for constructing utility-lists. An extensive performance study on real-life and synthetic datasets shows that the proposed algorithm is efficient both in terms of runtime and memory consumption and has excellent scalability.     

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.