Ayhan Demiriz

A framework for visualizing association mining results

Association mining is one of the most used data mining techniques due to interpretable and actionable results. In this study we propose a framework to visualize the association mining results, specifically frequent itemsets and association rules, as graphs. We demonstrate the applicability and usefulness of our approach through a Market Basket Analysis (MBA) case study where we visually explore the data mining results for a supermarket data set. In this case study we derive several interesting insights regarding the relationships among the items and suggest how they can be used as basis for decision making in retailing.

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.

Analyzing Price Data to Determine Positive and Negative Product Associations

This paper presents a simple method for mining both positive and negative association rules in databases using singular value decomposition (SVD) and similarity measures. In literature, SVD is used for summarizing matrices. We use transaction-item price matrix to generate so called ratio rules in the literature. Transaction-item price matrix is formed by using the price data of corresponding items from the sales transactions. Ratio rules are generated by running SVD on transaction-item price matrix. We then use similarity measures on a subset of rules found by Pareto analysis to determine positive and negative associations. The proposed method can present the positive and negative associations with their strengths. We obtain subsequent results using cosine and correlation similarity measures.

A framework for balanced service and cross-selling by using queuing science

Contact centers are complex call centers to handle large volume of inbound, outbound or both types of calls depending on the business purpose. Call centers assume the role of the primary contact medium for many companies from a wide range of industries with their customers or clients. Despite of being seen traditionally as adding cost to the companies’ bottom lines, call centers are now viewed by many companies to turn a service request into an opportunity to sell additional products and services. This sales attempt is called cross-selling. The opportunity to generate profit from an existing customer-base is a key factor for a successful call center. This paper introduces a framework for balancing cross-selling and service activities in a call center setting from a queuing science point of view. The main goal of this study is to introduce a framework to maximize a call center’s performance without degrading the service quality. Our framework is based on the usage of real-time queue characteristics, customer profile information and server-skill set information from a cross-sell point of view.

Linking behavioral patterns to personal attributes through data re-mining

A fundamental challenge in behavioral informatics is the development of methodologies and systems that can achieve its goals and tasks, including behavior pattern analysis. This study presents such a methodology, that can be converted into a decision support system, by the appropriate integration of existing tools for association mining and graph visualization. The methodology enables the linking of behavioral patterns to personal attributes, through the re-mining of colored association graphs that represent item associations. The methodology is described and mathematically formalized, and is demonstrated in a case study related with retail industry.

CPNoC: On Using Constraint Programming in Design of Network-on-Chip Architecture

NoC technology is composed of switched-based interconnections, where the communication resources are shared. Therefore, the optimal resource utilization is a crucial consideration for the efficient architecture designs. Application mapping and scheduling are important optimization problems. This paper studies the practicality of the Constraint Programming (CP) models on NoC architecture designs that effectively use a regular mesh with wormhole switching and the XY routing. The complexity of the CP models is compared to the earlier Mixed Integer Programming (MIP) models. Practical CP-based mapping and scheduling models are developed and the results are reported on the benchmark datasets. The results indicate that mapping and scheduling problems can be solved at near optimality even under relatively shorter run-time limits compared to those required by the MIP models.

Using constraint programming for the design of network-on-chip architectures

NoC technology is composed of packet-based interconnections, where the communication resources are distributed across the network. Therefore, the optimal resource utilization is a crucial consideration for efficient architectural designs. This paper studies the practicality of the Constraint Programming (CP) models for NoC architecture designs that effectively use a regular mesh with wormhole switching and the XY routing. The complexity of the CP models is compared with the earlier Mixed Integer Programming (MIP) models. Practical CP-based mapping and scheduling models are developed and results are reported on the benchmark datasets. Results indicate that mapping and scheduling problems can be solved at near optimality even under relatively shorter run-time limits as compared to those required by the MIP models.

Voltage island based heterogeneous NoC design through constraint programming

Display Omitted A two-stage Constraint Programming (CP) model was proposed for designing a heterogeneous Network-on-Chip (NoC).With shape, size constraints and proper assumptions, method was improved to solve Voltage-Frequency Island (VFI) problem.Our VFI implementation requires solving a single stage CP model.Real application data were used to conduct experimental studies to show the applicability of the models. This paper discusses heterogeneous Network-on-Chip (NoC) design from a Constraint Programming (CP) perspective and extends the formulation to solving Voltage-Frequency Island (VFI) problem. In general, VFI is a superior design alternative in terms of thermal constraints, power consumption as well as performance considerations. Given a Communication Task Graph (CTG) and subsequent task assignments for cores, cores are allocated to the best possible places on the chip in the first stage to minimize the overall communication cost among cores. We then solve the application scheduling problem to determine the optimum core types from a list of technological alternatives and to minimize the makespan. Moreover, an elegant CP model is proposed to solve VFI problem by mapping and grouping cores at the same time with scheduling the computation tasks as a limited capacity resource allocation model. The paper reports results based on real benchmark datasets from the literature.

On heterogeneous network-on-chip design based on constraint programming

Core mapping and application scheduling problems coupled with routing schemes are essential design considerations for an efficient Network-on-Chip (NoC) design. This paper discusses heterogeneous NoC design from a Constraint Programming (CP) perspective using a two-stage solution. Given a Communication Task Graph (CTG) and subsequent task assignments for cores, cores are allocated to the best possible places on the chip in the first stage in order to minimize the overall communication cost among cores. We then solve the application scheduling problem in the second stage to determine the optimum core types from a list of technological alternatives and to minimize the makespan i.e. time to complete all tasks. As a design extension, surface area constraint can be introduced to the underlying problem. The paper reports results based on real benchmark datasets from the literature.