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Book ChapterDOI

An Efficient Premiumness and Utility-Based Itemset Placement Scheme for Retail Stores

TL;DR: This paper proposes the notion of premiumness of slots in a given retail store, and discusses 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.
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.
Citations
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Journal ArticleDOI
TL;DR: This paper proposes a novel flexible and efficient index, designated as Slot Type Utility (STU) index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots, and conducts an extensive performance evaluation to demonstrate the overall effectiveness of the STU index.
Abstract: Utility mining has been emerging as an important area in data mining. While existing works on utility mining for retail businesses have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. Here, the slot size of a given item is the number of (integer) slots occupied by that item on the retail store shelves. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this paper considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be filled. Second, we propose a novel flexible and efficient index, designated as Slot Type Utility (STU) index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots. Third, we conducted an extensive performance evaluation using both real and synthetic datasets to demonstrate the overall effectiveness of the STU index in quickly retrieving the top-utility itemsets by considering a placement scheme in terms of execution time and utility (net revenue) as compared to recent existing schemes.

12 citations


Cites background from "An Efficient Premiumness and Utilit..."

  • ...Notably, our proposed scheme differs from the works in [31,32] because they consider different research problems with different underlying assumptions as well as context....

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  • ...maximization of the retailer by considering the variations in the premiumness of the retail slots [32]....

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Book ChapterDOI
27 Sep 2021
TL;DR: In this paper, the authors introduce the notion of urgency for retail itemset placement and propose the urgency-aware URI index for efficiently retrieving high-revenue and urgent itemsets of different sizes.
Abstract: Placement of items on the shelf space of retail stores signifcantly impacts the revenue of the retailer. Given the prevalence and popularity of medium-to-large-size retail stores, several research efforts have been made towards facilitating item/itemset placement in retail stores for improving retailer revenue. However, they do not consider the issue of urgency of sale of individual items. Hence, they cannot efficiently index, retrieve and place high-revenue itemsets in retail store slots in an urgency-aware manner. Our key contributions are two-fold. First, we introduce the notion of urgency for retail itemset placement. Second, we propose the urgency-aware URI index for efficiently retrieving high-revenue and urgent itemsets of different sizes. We discuss the URIP itemset placement scheme, which exploits URI for improving retailer revenue. We also conduct a performance evaluation with two real datasets to demonstrate that URIP is indeed effective in improving retailer revenue w.r.t. existing schemes.

4 citations

Proceedings ArticleDOI
06 Oct 2021
TL;DR: In this paper, a Product Expiry-Aware and Revenue-conscious itemset placement scheme is proposed for improving retailer revenue. But, the authors do not consider the time-period of expiry across items.
Abstract: Placement of items on the shelf space of retail stores significantly impacts the revenue of the retailer. Since customers typically tend to buy sets of items (i.e., itemsets) together, several research efforts have been undertaken towards facilitating itemset placement in retail stores for improving retailer revenue. However, they fail to consider that the time-period of expiry can vary across items i.e., some items expire sooner than others. This leads to loss of opportunity towards improving retailer revenue. Hence, we propose PEAR, which is a Product Expiry-Aware and Revenue-conscious itemset placement scheme for improving retailer revenue. Our key contributions are three-fold. First, we introduce the problem of addressing retail itemset placement when the items can be associated with different time-periods of expiry. Second, we propose the expiry-aware PEAR scheme for efficiently identifying and placing high-revenue itemsets for improving retailer revenue. Third, we conduct a performance study with two real datasets to demonstrate that PEAR is indeed effective in improving retailer revenue w.r.t. a reference scheme.

3 citations

Journal ArticleDOI
TL;DR: This work proposes an efficient framework for retrieval of high-revenue itemsets with a varying size and a varying degree of diversification, and proposes the kUI (kU tility I temset) index for quick and efficient retrieval of diverse top-λ high- re revenue itemsets.

3 citations

Proceedings ArticleDOI
01 Oct 2020
TL;DR: This work proposes the generalized utility itemset (GUI) index for retrieving generalized high-utility (revenue) itemsets and presents a framework, which leverages the GUI index towards retail product placement to improve revenue.
Abstract: Product placement in retail has a significant impact on the sales revenue of retailers. Hence, research efforts are being made to improve retailer revenue using high-utility pattern mining based product placement approaches. However, none of these existing approaches has explored generalized high-utility itemset mining for determining product placement in retail. The knowledge of generalized high-utility itemsets extracted from user purchase transactional database in conjunction with a product taxonomy can provide new insights about customer purchase behaviour. This work proposes the generalized utility itemset (GUI) index for retrieving generalized high-utility (revenue) itemsets. We also present a framework, which leverages the GUI index towards retail product placement to improve revenue. Our performance study using real datasets shows the effectiveness of our proposed scheme w.r.t. two existing schemes.

1 citations


Cites background from "An Efficient Premiumness and Utilit..."

  • ...The work in [8] addressed the problem of determining the placement of the itemsets in different types of slots with varied premiumness....

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References
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Proceedings Article
01 Jul 1998
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
Abstract: We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving thii problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems. We also show how the best features of the two proposed algorithms can be combined into a hybrid algorithm, called AprioriHybrid. Scale-up experiments show that AprioriHybrid scales linearly with the number of transactions. AprioriHybrid also has excellent scale-up properties with respect to the transaction size and the number of items in the database.

10,863 citations


"An Efficient Premiumness and Utilit..." refers background in this paper

  • ...Association rule mining approaches [8-10] use the downward closure property [8] for finding frequent itemsets above a support threshold [9], but they do not consider item utility....

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Journal ArticleDOI
16 May 2000
TL;DR: This study proposes a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develops an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth.
Abstract: Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns.In this study, we propose a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a highly condensed, much smaller data structure, which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent pattern mining methods.

6,118 citations


"An Efficient Premiumness and Utilit..." refers background in this paper

  • ...Alternatively, the retailer could fill the highly-premium slots with the most frequent itemsets [9-10]....

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  • ...Association rule mining approaches [8-10] use the downward closure property [8] for finding frequent itemsets above a support threshold [9], but they do not consider item utility....

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Book ChapterDOI
10 Jan 1999
TL;DR: This paper proposes a new algorithm, called A-Close, using a closure mechanism to find frequent closed itemsets, and shows that this approach is very valuable for dense and/or correlated data that represent an important part of existing databases.
Abstract: In this paper, we address the problem of finding frequent itemsets in a database. Using the closed itemset lattice framework, we show that this problem can be reduced to the problem of finding frequent closed itemsets. Based on this statement, we can construct efficient data mining algorithms by limiting the search space to the closed itemset lattice rather than the subset lattice. Moreover, we show that the set of all frequent closed itemsets suffices to determine a reduced set of association rules, thus addressing another important data mining problem: limiting the number of rules produced without information loss. We propose a new algorithm, called A-Close, using a closure mechanism to find frequent closed itemsets. We realized experiments to compare our approach to the commonly used frequent itemset search approach. Those experiments showed that our approach is very valuable for dense and/or correlated data that represent an important part of existing databases.

1,513 citations


"An Efficient Premiumness and Utilit..." refers background in this paper

  • ...Alternatively, the retailer could fill the highly-premium slots with the most frequent itemsets [9-10]....

    [...]

  • ...Association rule mining approaches [8-10] use the downward closure property [8] for finding frequent itemsets above a support threshold [9], but they do not consider item utility....

    [...]

Proceedings ArticleDOI
29 Oct 2012
TL;DR: This paper proposes an algorithm, called HUI-Miner (High Utility Itemset Miner), which can efficiently mine high utility itemsets from the utility-lists constructed from a mined database and compares it with the state-of-the-art algorithms on various databases.
Abstract: High utility itemsets refer to the sets of items with high utility like profit in a database, and efficient mining of high utility itemsets plays a crucial role in many real-life applications and is an important research issue in data mining area. To identify high utility itemsets, most existing algorithms first generate candidate itemsets by overestimating their utilities, and subsequently compute the exact utilities of these candidates. These algorithms incur the problem that a very large number of candidates are generated, but most of the candidates are found out to be not high utility after their exact utilities are computed. In this paper, we propose an algorithm, called HUI-Miner (High Utility Itemset Miner), for high utility itemset mining. HUI-Miner uses a novel structure, called utility-list, to store both the utility information about an itemset and the heuristic information for pruning the search space of HUI-Miner. By avoiding the costly generation and utility computation of numerous candidate itemsets, HUI-Miner can efficiently mine high utility itemsets from the utility-lists constructed from a mined database. We compared HUI-Miner with the state-of-the-art algorithms on various databases, and experimental results show that HUI-Miner outperforms these algorithms in terms of both running time and memory consumption.

539 citations


"An Efficient Premiumness and Utilit..." refers background or methods in this paper

  • ...Hence, utility mining approaches [11-19] have been proposed for finding highutility patterns, but they do not satisfy the downward closure property....

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  • ...Existing utility mining approaches focus on the following aspects: determining minimal high-utility itemsets [11], creating representations of high-utility itemsets [12], reducing candidate itemset generation overheads by using the utility-list [16] and the UP-Tree [18], and identifying the upperbounds and heuristics for pruning the search space [14-15]....

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  • ...The HUI-Miner algorithm [16] uses the utility-list for storing information (including utility) about the itemsets for avoiding expensive candidate itemset generation....

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Proceedings ArticleDOI
19 Nov 2003
TL;DR: A new pruning strategy based on utilities that allow pruning of low utility itemsets to be done by means of a weaker but antimonotonic condition is developed and shows that it does not require a user specified minimum utility and hence is effective in practice.
Abstract: Traditional association rule mining algorithms only generate a large number of highly frequent rules, but these rules do not provide useful answers for what the high utility rules are. We develop a novel idea of top-K objective-directed data mining, which focuses on mining the top-K high utility closed patterns that directly support a given business objective. To association mining, we add the concept of utility to capture highly desirable statistical patterns and present a level-wise item-set mining algorithm. With both positive and negative utilities, the antimonotone pruning strategy in Apriori algorithm no longer holds. In response, we develop a new pruning strategy based on utilities that allow pruning of low utility itemsets to be done by means of a weaker but antimonotonic condition. Our experimental results show that our algorithm does not require a user specified minimum utility and hence is effective in practice.

443 citations


"An Efficient Premiumness and Utilit..." refers background in this paper

  • ...Hence, utility mining approaches [11-19] have been proposed for finding highutility patterns, but they do not satisfy the downward closure property....

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  • ...The work in [19] prunes away lowutility itemsets and considers business goals as well when evaluating utility values....

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