N. R. Achuthan

CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach

Frequent pattern mining discovers patterns in transaction databases based only on the relative frequency of occurrence of items without considering their utility. For many real world applications, however, utility of itemsets based on cost, profit or revenue is of importance. The utility mining problem is to find itemsets that have higher utility than a user specified minimum. Unlike itemset support in frequent pattern mining, itemset utility does not have the anti-monotone property and so efficient high utility mining poses a greater challenge. Recent research on utility mining has been based on the candidate-generation-and-test approach which is suitable for sparse data sets with short patterns, but not feasible for dense data sets or long patterns. In this paper we propose a new algorithm called CTU-Mine that mines high utility itemsets using the pattern growth approach. We have tested our algorithm on several dense data sets, compared it with the recent algorithms and the results show that our algorithm works efficiently.

Efficient mining of high utility itemsets from large datasets

High utility itemsets mining extends frequent pattern mining to discover itemsets in a transaction database with utility values above a given threshold. However, mining high utility itemsets presents a greater challenge than frequent itemset mining, since high utility itemsets lack the anti-monotone property of frequent itemsets. Transaction Weighted Utility (TWU) proposed recently by researchers has anti-monotone property, but it is an overestimate of itemset utility and therefore leads to a larger search space. We propose an algorithm that uses TWU with pattern growth based on a compact utility pattern tree data structure. Our algorithm implements a parallel projection scheme to use disk storage when the main memory is inadequate for dealing with large datasets. Experimental evaluation shows that our algorithm is more efficient compared to previous algorithms and can mine larger datasets of both dense and sparse data containing long patterns.

Decision Support Tool for Retail Shelf Space Optimization

Efficient allocation of shelf space and product assortment can significantly improve a retailers profitability. This paper addresses the problem from the perspective of an independent franchise retailer. A Category Management Decision Support Tool (CMDST) is proposed that efficiently generates optimal shelf space allocations and product assortments by using the existing scarce resources, resulting in increased profitability. CMDST utilizes two practical integrated category management models that maximize the total net profit in terms of decision variables expressing product assortment, shelf space allocation, review period, and order quantity. The implementation of the models demonstrates their robustness and that the net profit can be significantly increased when compared to the current industry practice.

A Retail Category Management Model Integrating Shelf Space And Inventory Levels

A retail category management model that considers the interplay of optimal product assortment decisions, space allocation and inventory quantities is presented in this paper. Specifically, the proposed model maximizes the total net profit in terms of decision variables expressing product assortment, shelf space allocation and common review period. The model takes into consideration several constraints such as the available shelf space, backroom inventory space, retailers financial resources, and estimates of rate of demand for products based on shelf space allocation and competing products. The review period can take any values greater than zero. Results of the proposed model were compared with the results of the current industry practice for randomly generated product assortments of size six, ten and fourteen. The model also outperformed the literature benchmark. The paper demonstrates that the optimal common review period is flexible enough to accommodate the administrative restrictions of delivery schedules for products, without significantly deviating from the optimal solution.

On mixed Ramsey numbers

For positive integers m and n the classical ramsey number r(m, n) is the least positive integer p such that if G is any graph of order p then either G contains a subgraph isomorphic to Km or the complement G of G contains a subgraph isomorphic to Kn. Some authors have considered the concept of mixed ramsey numbers. Given a graph theoretic parameter f, an integer m and a graph H, the mixed ramsey number v(f; m; H) is defined as the least positive integer p such that if G is any graph of order p, then either f(G) ⩾ m or G contains a subgraph isomorphic to H. In this paper we consider the problem of determining the mixed ramsey numbers for vertex linear arboricity and some other generalizations of chromatic number. We discuss the above problem for various structures H such as the complete graph, the claw, the path and the tree. Further, we study the generalized mixed ramsey number v(f;m1, m2,…, m1; Hl + 1, Hl + 2,…, Hk), where the edge set of the complete graph is partitioned into k sets.

Mining value-based item packages – an integer programming approach

Traditional methods for discovering frequent patterns from large databases assume equal weights for all items of the database. In the real world, managerial decisions are based on economic values attached to the item sets. In this paper, we first introduce the concept of the value based frequent item packages problems. Then we provide an integer linear programming (ILP) model for value based optimization problems in the context of transaction data. The specific problem discussed in this paper is to find an optimal set of item packages (or item sets making up the whole transaction) that returns maximum profit to the organization under some limited resources. The specification of this problem allows us to solve a number of practical decision problems, by applying the existing and new ILP solution techniques. The model has been implemented and tested with real life retail data. The test results are reported in the paper.

Estimating Sufficient Sample Sizes for Approximate Decision Support Queries

Sampling schemes for approximate processing of highly selective decision support queries need to retrieve sufficient number of records that can provide reliable results within acceptable error limits. The k-MDI tree is an innovative index structure that supports drawing rich samples of relevant records for a given set of dimensional attribute ranges. This paper describes a method for estimating sufficient sample sizes for decision support queries based on inverse simple random sampling without replacement (SRSWOR). Combined with a k-MDI tree index, this method is shown to offer a reliable approach to approximate query processing for decision support.