Re-mining item associations: methodology and a case study in apparel retailing

Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques

Re-mining positive and negative association mining results

Positive and negative association mining are well-known and extensively studied data mining techniques to analyze market basket data. Efficient algorithms exist to find both types of association, separately or simultaneously. Association mining is performed by operating on the transaction data. Despite being an integral part of the transaction data, the pricing and time information has not been incorporated into market basket analysis so far, and additional attributes have been handled using quantitative association mining. In this paper, a new approach is proposed to incorporate price, time and domain related attributes into data mining by re-mining the association mining results. The underlying factors behind positive and negative relationships, as indicated by the association rules, are characterized and described through the second data mining stage re-mining. The applicability of the methodology is demonstrated by analyzing data coming from apparel retailing industry, where price markdown is an essential tool for promoting sales and generating increased revenue

Quantitative Assessment of Salivary Gland Parenchymal Vascularization Using Power Doppler Ultrasound and Superb Microvascular Imaging: A Potential Tool in the Diagnosis of Sjögren’s Syndrome

Background: Primary Sjögren’s syndrome is a chronic inflammatory autoimmune disease. Minor salivary gland biopsy is the gold standard for the diagnosis of primary Sjögren’s syndrome. Superb microvascular imaging, power Doppler ultrasound, and color Doppler of the salivary glands represent non-invasive, non-irradiating modality for evaluating the vascularity of the salivary glands in the diagnosis and follow-up of primary Sjögren’s syndrome. Aims: To evaluate the efficacy of superb microvascular imaging and vascularity index in salivary glands for the sonographic diagnosis of primary Sjögren’s syndrome. Study Design: Prospective case-control study. Methods: Twenty participants with primary Sjögren’s syndrome and 20 healthy subjects were included in the study. Both parotid glands and submandibular glands were evaluated by superb microvascular imaging, power Doppler ultrasound, and color Doppler. The diagnostic accuracy of superb microvascular imaging was compared using these techniques. Results: In the patient group, the vascularity index values of superb microvascular imaging in parotid glands and submandibular glands were 3.5±1.66, 5.06±1.94, respectively. While the same values were 1.0±0.98 and 2.44±1.34 in the control group (p?0.001). In the patient group, the vascularity index values of power Doppler ultrasound in parotid glands and submandibular glands were 1.3±1.20 and 2.59±1.82, respectively. While the same values were 0.3±0.32 and 0.85±0.68 in the control group (p?0.001). The superb microvascular imaging vascularity index cut-off value for the diagnosis of primary Sjögren’s syndrome in parotid glands that maximizes the accuracy was 1.85 (area under the curve: 0.906; 95% confidence interval: 0.844, 0.968), and its sensitivity and specificity were 87.5% and 72.5%, respectively. While the superb microvascular imaging vascularity index cut-off value for the diagnosis of primary Sjögren’s syndrome in submandibular gland that maximizes the accuracy was 3.35 (area under the curve: 0.873; 95% confidence interval: 0.800, 0.946), its sensitivity and specificity were 82.5% and 70%, respectively. Conclusion: Superb microvascular imaging with high reproducibility of the vascularity index has a higher sensitivity and specificity than the power Doppler ultrasound in the diagnosis of primary Sjögren’s syndrome. It can be a noninvasive technique in the diagnosis of primary Sjögren’s syndrome when used with clinical, laboratory and other imaging methods

Türk hazır giyim sanayi için veri madenciliği tabanlı bir kalıcı indirim yönetim sistemi prototipi

Tekstil sektörünün önemli bir alt-sektörü olan hazır giyim sanayindeki eğilim, üretici firmaların aynı zamanda perakendeci olarak da faaliyet göstermeleridir (LC Waikiki, Mavi Jeans, v.b.). Son yıllarda özellikle A.B.D’de perakende sektöründeki karar vericilere yardımcı olmak amacı ile geliştirilen perakende analitiği yazılımları yaygın olarak kullanılmaya başlanmıştır (Lightship Partners, 2009). Yerli hazır giyim perakendecilerimizin yabancı rakipleri ile rekabet edebilmek ve onların önüne geçebilmek için benzer perakende yönetimi karar destek sistemlerine ihtiyaç duymaktadır. Perakende analitiği yazılımların yerine getirdiği en önemli işlevlerden birisi kalıcı indirim eniyilemesidir (markdown optimization). Kalıcı indirim, satış miktarları azalan veya azalmaya yüz tutmuş olan ürünlerin satışlarını arttırmak için yapılan ve ürün fiyatı bir kez indirildikten sonra tekrar indirimli fiyatın üzerine çıkılamayan bir indirim biçimidir. Kalıcı indirimlerin en sık kullanıldığı sektörlerden başında hazır giyim sektörü gelmektedir. Kalıcı indirim eniyilemesi literatüründe yer alan çalışmalar ve pazarda bulunan ticari yazılımlar, kalıcı indirim eniyilemesinde ürünlerin taleplerinin birbirinden bağımsız olduğunu varsaymakta ve ürün talepleri arasındaki fiyata bağlı tamamlayıcı ve ikame etkilerini (çapraz fiyat esneklikleri) göz ardı etmektedir. Oysa ürünler arası ilişkiler ve etkileşimler de kalıcı indirim en iyilenmesinde dikkate alınması gereken önemli bir noktadır. Bu projede, Türkiye’nin en büyük hazır giyim perakendecisi olan LC Waikiki tarafından sağlanan ürün satış bilgileri kullanılarak, veri madenciliği yardımı ile, arasında ikame ve tamamlayıcı etkiler olması muhtemel ürün gruplarını bulan, aynı grupta yer alan ürünlerin fiyatlarına bağlı olarak ürün taleplerini tahmin eden ve yaklaşık dinamik programlama yardımı ile ürün kalıcı indirim oranlarını ve bu oranların zamanlamasını belirleyen bir kalıcı indirim karar destek sistemi prototipi geliştirilmektedir.A major trend in the apparel sector, which is a sub-sector of textile industry, is the entrance of apparel producers into the consumer market as retailers (LC Waikiki, Mavi Jeans, etc.). In recent years, especially in the USA, retails analytics software have gained increased popularity for helping decision makers. Turkish apparel retailers need similar decision support systems to be able to compete with international apparel chains. One of the most significant functions of retail analytics software is markdown optimization, which decides on the level of markdown price for items throughout a season. Markdown is a special type of discount, where the price is monotonically non-decreasing throughout the season. Existing academic research on markdown optimization and business software for retail analytics assume independence between the demands of items, ignoring the complementarity and substitute effects between them. However, such associations and interactions between items are important, and should be taken into account during markdown optimization. In this project, the goal is to construct a methodology and a prototype system for markdown optimization. The developed methodology starts with finding the complementary and substitute products through positive and negative association mining, respectively. Then the demand of each item is forecasted based on the set of items it is associated with. Finally, approximate dynamic programming is used to compute markdown ratios and their timing. The methodology is tested with real world data from LC Waikiki, the largest apparel retail chain in Turkey.Publisher's Versio

Markdown Optimization via Approximate Dynamic Programming

We consider the markdown optimization problem faced by the leading apparel retail chain. Because of substitution among products the markdown policy of one product affects the sales of other products. Therefore, markdown policies for product groups having a significant crossprice elasticity among each other should be jointly determined. Since the state space of the problem is very huge, we use Approximate Dynamic Programming. Finally, we provide insights on the behavior of how each product price affects the markdown policy

Analysis of cross-price effects on markdown policies by using function approximation techniques

Markdown policies for product groups having significant cross-price elasticity among each other should be jointly determined. However, finding optimal policies for product groups becomes computationally intractable as the number of products increases. Therefore, we formulate the problem as a Markov decision process and use approximate dynamic programming approach to solve it. Since the state space is multidimensional and very large, the number of iterations required to learn the state values is enormous. Therefore, we use aggregation and neural networks in order to approximate the value function and to determine the optimal markdown policies approximately. In a numerical study, we provide insights on the behavior of markdown policies when one product is expensive, the other is cheap and both have the same price. We also provide insights and compare the markdown policies for the cases in which there is a substitution effect between products and the products are independent. (C) 2013 Elsevier B.V. All rights reserved

Performance evaluation and control of flexible work crews and machines in manufacturing.

In this dissertation we study production systems with flexible work force and machines. In chapter 2, we consider a production system with multiple machines each serving a different group of products. The machines require setups to switch from one product type to another. Setup operations are performed by a limited number of setup crews who are cross-trained to perform several types of setup operations. We develop a model that takes delays caused by setup crew unavailability into account and obtain the approximate average waiting time for each product type. In a numerical study we show that our approximation performs well. We also provide insights into the importance of explicitly modelling the extent to which the level of cross-training affects performance. In chapter 3 we consider a production system with two machines and two product types. Machine 1 is a dedicated server and produces product 1 only. Machine 2 is a flexible server which is primarily assigned to product 2 and can also produce product 1 if needed. We characterize the form of the optimal policy that maximizes the average profit in machine 1 and machine 2 in make-to-stock and make-to-order production regimes and show that the optimal policies are of threshold type. We also show monotonicity of production and idling curves with respect to system parameters. Finally, in a numerical study we provide insights on the benefit of flexibility and the conditions under which the models considered in chapter 3 outperform a single flexible machine or a two dedicated-machine system.Ph.D.Applied SciencesIndustrial engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/124111/2/3121978.pd

A reinforcement learning algorithm with fuzzy approximation for semi Markov decision problems

Real life stochastic problems are generally large-scale, difficult to model, and therefore, suffer from the curses of dimensionality. Such problems cannot be solved by classical optimization methods. This paper presents a reinforcement learning algorithm using a fuzzy inference system, ANFIS to find an approximate solution for semi Markov decision problems (SMDPs). The performance of the developed algorithm is measured and compared to a classical reinforcement algorithm, SMART in a numerical example. Our numerical examples show that the developed algorithm converges significantly faster as the problem size increases and the average cost calculated by the algorithm gets closer to that of SMART as number of epochs used in the developed algorithm is increased