Abstract

This work presents a Lyapunov-based approach to adaptive control of uncertain Euler-Lagrange (EL) systems in a slow switching scenario. Fundamental trade-offs arising from considering uncertain dynamics with unknown uncertainty bounds are presented and discussed. Contrary to the nonswitched scenario, the use of acceleration feedback seems to be unavoidable in the switched scenario: this is due to the fact that an acceleration feedback and an appropriate Lyapunov function must be adopted to make the switching law independent from the unknown uncertainty bounds. In the absence of such feedback or using different Lyapunov functions, a stabilizing switching law would exist but could not be determined as it would depend on an unknown uncertainty bound.

Abstract

Periodic-frequent pattern mining is an important model in data mining. The popular adoption and successful industrial application of this model has been hindered by the following two limitations: (i) The periodic-frequent pattern model implicitly assumes that all transactions within the data occur at a uniform time interval. This assumption limits the model’s applicability as the transactions in many real-world databases occur at irregular time intervals. (ii) Finding periodic-frequent patterns in very large databases is a memory intensive process because its mining algorithm has to maintain a list structure to record all timestamps at which an itemset has appeared in the whole data. This paper makes an effort to address these two limitations. A flexible model of periodic-frequent pattern in temporal databases has been described to address the former issue. In order to address the latter issue, a novel concept known as period summary has been introduced to effectively capture the temporal occurrence information of an itemset in a database. A new tree structure, called Periodic Summary-tree (PS-tree), has be introduced to record the temporal occurrence information of an itemset in a temporal database. A patterngrowth algorithm has also been described to find all periodic-frequent patterns from PStree. Experimental results demonstrate that the proposed algorithm is efficient.

Abstract

The coffee white stem borer (CWSB), Xylotrechus quadripes (Coleoptera: Cerambycidae), is a major pest of arabica coffee causing considerable losses to the growers and its control has been an issue of significance in the pest management. Among the integrated management strategies, spraying of Chlorpyrifos 20EC at appropriate time will prevent the development of pest. This study was aimed to find out the alternative chemical for Chlorpyrifos 20EC which is being used for more than a decade. The laboratory experiments indicated that insecticides like Phenthoate 50EC, Fipronil 5SC, Thiamethoxam 12.6 + Lambda-Cyhalothrin 9.5 ZC, Chlorpyrifos 50EC + Cypermethrin 5EC and Chlorpyrifos 20EC caused 100% mortality of eggs and it is significantly on par with Ethioprole + Imidacloprid 80WG (98%). In case of neonate larvae, 100 percent mortality was observed in the treatments viz., Phenthoate 50EC, Imidacloprid 17.8SL, Fipronil 5SC, Thiamethoxam 12.6 + Lambda-Cyhalothrin 9.5% ZC, Ethioprole + Imidacloprid 80 WG, Chlorpyrifos 50EC + Cypermethrin 5EC and Chlorpyrifos 20EC. The least mortality was observed in Indoxacarb 14.5 SC among the insecticides tested. The field experiment data revealed that maximum ovicidal and larvicidal action was observed to Chlorpyrifos 50EC + Cypermethrin 5EC and Chlorpyrifos 20EC followed by Phenthoate 50EC and Fipronil 5SC. The least mortality of eggs and neonate larvae was recorded to Novaluron 10 EC and Indoxacarb 14.5 SC. It is, therefore, Chlorpyrifos 50EC + Cypermethrin 5EC, can be utilized as a valuable alternate for Chlorpyrifos 20EC in integrated management of coffee white stem borer.

Abstract

High utility itemset mining is an important model with many real-world applications. But the popular adoption and successful industrial application of this model has been hindered by the following two limitations: (i) computational expensiveness of the model and (ii) infrequent itemsets may be output as high utility itemsets. This paper makes an effort to address these two limitations. A generic high utility-frequent itemset model is introduced to find all itemsets in the data that satisfy user-specified minimum support and minimum utility constraints. Two new pruning measures, named cutoff utility and suffix utility, are introduced to reduce the computational cost of finding the desired itemsets. A single phase fast algorithm, called High Utility Frequent Itemset Miner (HU-FIMi), is introduced to discover the itemsets efficiently. Experimental results demonstrate that the proposed algorithm is efficient.

Abstract

In real-world databases, high utility itemset (HUI) is an important class of regularities. Most previous studies have focused on mining HUIs in transactional databases and did not consider the spatiotemporal characteristics of items. In this study, a more flexible model of spatial HUIs (SHUIs) that exist in spatiotemporal databases is proposed. In a spatiotemporal database (STD), an itemset is said to be an SHUI if its utility is not less than a user-specified minimum utility and the distance between any two of its items is not more than a user-specified maximum distance. Identifying SHUIs is very challenging because the generated itemsets do not satisfy the anti-monotonic property. In this study, we present two novel pruning techniques for reducing computational costs. Moreover, a fast single scan algorithm is presented for effectively evaluating all SHUIs in a STD. Furthermore, two case studies are presented, in which the proposed model is used to identify useful information in traffic congestion data and air pollution data.

Abstract

In retail stores, the placement of items on the shelf space significantly impacts the sales of items. In particular, the probability of sales of a given item is typically considerably higher when it is placed in a premium (i.e., highly visible/easily accessible) slot as opposed to a non-premium slot. In this paper, we address the problem of maximizing the revenue for the retailer by determining the placement of the itemsets in different types of slots with varied premiumness such that each item is placed at least once in any of the slots. We first propose the notion of premiumness of slots in a given retail store. Then we discuss a framework for efficiently identifying itemsets from a transactional database and placing these itemsets by mapping itemsets with different revenue to slots with varied premiumness for maximizing retailer revenue. Our performance evaluation on both synthetic and real datasets demonstrate that the proposed scheme indeed improves the retailer revenue by up to 45% w.r.t. a recent existing scheme.

Abstract

While the problems of finding the shortest path and k-shortest paths have been extensively researched, the research community has been shifting its focus towards discovering and identifying paths based on user preferences. Since users naturally follow some of the paths more than other paths, the popularity of a given path often reflects such user preferences. Moreover, users typically prefer diverse paths over similar paths for gaining flexibility in path selection. Given a set of user traversals in a road network and a set of paths between a given source and destination pair, we propose a scheme based on transactional modeling and pattern mining for performing top-k ranking of these paths based on both path popularity and path diversity. Our performance evaluation with a real dataset demonstrates the effectiveness of the proposed scheme.

Abstract

High Utility Itemset Mining (HUIM) is an important model with many real-world applications. Given a (non-binary) transactional database and an external utility database, the aim of HUIM is to discover all itemsets within the data that satisfy the user-specified minimum utility (minUtil) constraint. The popular adoption and successful industrial application of HUIM has been hindered by the following two limitations: (i) HUIM does not allow external utilities of items to vary over time and (ii) HUIM algorithms are inadequate to find recurring customer purchase behavior. This paper introduces a flexible model of Partial Periodic High Utility Itemset Mining (PPHUIM) to address these two problems. The goal of PPHUIM is to discover only those interesting high utility itemsets that are occurring at regular intervals in a given temporal database. An efficient depth-first search algorithm, called PPHUI-Miner (Partial Periodic High Utility Itemset-Miner), has been proposed to enumerate all partial periodic high-utility itemsets in temporal databases. Experimental results show that the proposed algorithm is efficient.

Abstract

Visibility computation is critical in spatial databases for realizing various interesting and diverse applications such as defence-related surveillance, identifying interesting spots in tourist places and online warcraft games. Existing works address the problem of identifying individual locations for maximizing the visibility of a given target object. However, in case of many applications, a set of locations may be more effective than just individual locations towards maximizing the visibility of the given target object. In this paper, we introduce the Multi-Location Visibility (MLV) query. An MLV query determines the top-k query locations from which the visibility of a given target object can be maximized. We propose a portion-based transactional framework and coverage pattern mining based algorithm to process MLV queries. Our performance evaluation with real datasets demonstrates the effectiveness of the proposed scheme in terms of query processing time, pruning efficiency and target object visibility w.r.t. a recent existing scheme.

Abstract

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large databases. Typically, pattern mining is carried out from huge databases, which tend to get updated several times. Consequently, as a given database is updated, some of the patterns discovered may become invalid, while some new patterns may emerge. This has motivated significant research efforts in the area of Incremental Mining. The goal of incremental mining is to efficiently and incrementally mine patterns when a database is updated as opposed to mining all of the patterns from scratch from the complete database. Incidentally, research efforts are being made to develop incremental pattern mining algorithms for extracting different kinds of patterns such as frequent patterns, sequential patterns and utility patterns. However, none of the existing works addresses incremental mining in the context of coverage patterns, which has important applications in areas such as banner advertising, search engine advertising and graph mining. In this regard, the main contributions of this work are three-fold. First, we introduce the problem of incremental mining in the context of coverage patterns. Second, we propose the IncCMine algorithm for efficiently extracting the knowledge of coverage patterns when incremental database is added to the existing database. Third, we performed extensive experiments using two real-world click stream datasets and one synthetic dataset. The results of our performance evaluation demonstrate that our proposed IncCMine algorithm indeed improves the performance significantly w.r.t. the existing CMine algorithm. Index Terms—Data mining, Coverage patterns, Incremental mining, Knowledge discovery