Decision making with association rule mining and clustering in supply chains

This paper deals with data mining applications for the supply chain inventory management. ABC characterization is typically utilized for stock items arrangement on the grounds that the quantity of stock items is large to the point that it is not computationally practical to set stock and admin-istration control rules for every individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could influence the sale of other items (cross selling effect). Consequently, within each cluster, the inventories should be classified. In this paper, a modified approach is proposed considering both cross-selling effect and clusters to rank stock items. A numerical case is utilized to clarify the new ap-proach. It is represented that by utilizing this modified approach; the ranking of items may get influenced bringing about higher profits

Interaction of descriptive and predictive analytics with product networks: The case of Sam's club

Due to the fact that there are massive amounts of available data all around the world, big data analytics has become an extremely important phenomenon in many disciplines. As the data grow, the need for businesses to achieve more reliable and accurate data-driven management decisions and to create value with big data applications grows as well. That is the reason why big data analytics becomes a primary tech priority today. In this thesis, initially we used a two-stage clustering algorithms in the customer segmentation setting. After the clustering stage, the customer lifetime value (CLV) of clusters were calculated based on the purchasing behaviors of the customers in order to reveal managerial insights and develop marketing strategies for each segment. At the second stage, we used HITS algorithm in product network analysis to achieve valuable insights from generated patterns, with the aim of discovering cross-selling e ects, identifying recurring purchasing patterns, and trigger products within the networks. This is important for practitioners in real-life application in terms of emphasizing the relatively important transactions by ranking them with corresponding item sets. From practical point of view, we foresee that our proposed methodology is adaptable and applicable to other similar businesses throughout the world, providing a road map for the potential application

A study of frequent pattern and association rule mining: with applications in inventory update and marketing.

Wong, Chi-Wing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 149-153).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- MPIS --- p.3Chapter 1.2 --- ISM --- p.5Chapter 1.3 --- MPIS and ISM --- p.5Chapter 1.4 --- Thesis Organization --- p.6Chapter 2 --- MPIS --- p.7Chapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Related Work --- p.10Chapter 2.2.1 --- Item Selection Related Work --- p.11Chapter 2.3 --- Problem Definition --- p.22Chapter 2.3.1 --- NP-hardness --- p.25Chapter 2.4 --- Cross Selling Effect by Association Rules --- p.28Chapter 2.5 --- Quadratic Programming Method --- p.32Chapter 2.6 --- Algorithm MPIS_Alg --- p.41Chapter 2.6.1 --- Overall Framework --- p.43Chapter 2.6.2 --- Enhancement Step --- p.47Chapter 2.6.3 --- Implementation Details --- p.48Chapter 2.7 --- Genetic Algorithm --- p.60Chapter 2.7.1 --- Crossover --- p.62Chapter 2.7.2 --- Mutation --- p.64Chapter 2.8 --- Performance Analysis --- p.64Chapter 2.8.1 --- Preparation Phase --- p.65Chapter 2.8.2 --- Main Phase --- p.69Chapter 2.9 --- Experimental Result --- p.77Chapter 2.9.1 --- Tools for Quadratic Programming --- p.77Chapter 2.9.2 --- Partition Matrix Technique --- p.78Chapter 2.9.3 --- Data Sets --- p.81Chapter 2.9.4 --- Empirical Study for GA --- p.84Chapter 2.9.5 --- Experimental Results --- p.92Chapter 2.9.6 --- Scalability --- p.102Chapter 2.10 --- Conclusion --- p.106Chapter 3 --- ISM --- p.107Chapter 3.1 --- Introduction --- p.107Chapter 3.2 --- Related Work --- p.108Chapter 3.2.1 --- Network Model --- p.108Chapter 3.3 --- Problem Definition --- p.112Chapter 3.4 --- Association Based Cross-Selling Effect --- p.117Chapter 3.5 --- Quadratic Programming --- p.118Chapter 3.5.1 --- Quadratic Form --- p.119Chapter 3.5.2 --- Algorithm --- p.128Chapter 3.5.3 --- Example --- p.129Chapter 3.6 --- Hill-Climbing Approach --- p.134Chapter 3.6.1 --- Efficient Calculation of Formula of Profit Gain --- p.134Chapter 3.6.2 --- FP-tree Implementation --- p.135Chapter 3.7 --- Empirical Study --- p.136Chapter 3.7.1 --- Data Set --- p.137Chapter 3.7.2 --- Experimental Results --- p.138Chapter 3.8 --- Conclusion --- p.141Chapter 4 --- Conclusion --- p.147Bibliography --- p.15

A framework for personalized dynamic cross-selling in e-commerce retailing

Cross-selling and product bundling are prevalent strategies in the retail sector. Instead of static bundling offers, i.e. giving the same offer to everyone, personalized dynamic cross-selling generates targeted bundle offers and can help maximize revenues and profits. In resolving the two basic problems of dynamic cross-selling, which involves selecting the right complementary products and optimizing the discount, the issue of computational complexity becomes central as the customer base and length of the product list grows. Traditional recommender systems are built upon simple collaborative filtering techniques, which exploit the informational cues gained from users in the form of product ratings and rating differences across users. The retail setting differs in that there are only records of transactions (in period X, customer Y purchased product Z). Instead of a range of explicit rating scores, transactions form binary datasets; 1-purchased and 0-not-purchased. This makes it a one-class collaborative filtering (OCCF) problem. Notwithstanding the existence of wider application domains of such an OCCF problem, very little work has been done in the retail setting. This research addresses this gap by developing an effective framework for dynamic cross-selling for online retailing. In the first part of the research, we propose an effective yet intuitive approach to integrate temporal information regarding a product\u27s lifecycle (i.e., the non-stationary nature of the sales history) in the form of a weight component into latent-factor-based OCCF models, improving the quality of personalized product recommendations. To improve the scalability of large product catalogs with transaction sparsity typical in online retailing, the approach relies on product catalog hierarchy and segments (rather than individual SKUs) for collaborative filtering. In the second part of the work, we propose effective bundle discount policies, which estimate a specific customer\u27s interest in potential cross-selling products (identified using the proposed OCCF methods) and calibrate the discount to strike an effective balance between the probability of the offer acceptance and the size of the discount. We also developed a highly effective simulation platform for generation of e-retailer transactions under various settings and test and validate the proposed methods. To the best of our knowledge, this is the first study to address the topic of real-time personalized dynamic cross-selling with discounting. The proposed techniques are applicable to cross-selling, up-selling, and personalized and targeted selling within the e-retail business domain. Through extensive analysis of various market scenario setups, we also provide a number of managerial insights on the performance of cross-selling strategies

HealthTrust: Assessing the Trustworthiness of Healthcare Information on the Internet

As well recognized, healthcare information is growing exponentially and is made more available to public. Frequent users such as medical professionals and patients are highly dependent on the web sources to get the appropriate information promptly. However, the trustworthiness of the information on the web is always questionable due to the fast and augmentative properties of the Internet. Most search engines provide relevant pages to given keywords, but the results might contain some unreliable or biased information. Consequently, a significant challenge associated with the information explosion is to ensure effective use of information. One way to improve the search results is by accurately identifying more trustworthy data. Surprisingly, although trustworthiness of sources is essential for a great number of daily users, not much work has been done for healthcare information sources by far. In this dissertation, I am proposing a new system named HealthTrust, which automatically assesses the trustworthiness of healthcare information over the Internet. In the first phase, an unsupervised clustering using graph topology, on our collection of data is employed. The goal is to identify a relatively larger and reliable set of trusted websites as a seed set without much human efforts. After that, a new ranking algorithm for structure-based assessment is adopted. The basic hypothesis is that trustworthy pages are more likely to link to trustworthy pages. In this way, the original set of positive and negative seeds will propagate over the Web graph. With the credibility-based discriminators, the global scoring is biased towards trusted websites and away from untrusted websites. Next, in the second phase, the content consistency between general healthcare-related webpages and trusted sites is evaluated using information retrieval techniques to evaluate the content-semantics of the webpage with respect to the medical topics. In addition, graph modeling is employed to generate contents-based ranking for each page based on the sentences in the seed pages. Finally, in order to integrate the two components, an iterative approach that integrates the credibility assessments from structure-based and content-based methods to give a final verdict - a HealthTrust score for each webpage is exploited. I demonstrated the first attempt to integrate structure-based and content-based approaches to automatically evaluate the credibility of online healthcare information through HealthTrust and make fundamental contributions to both information retrieval and healthcare informatics communities