The remainder of the paper is organized as

The remainder of the paper is organized as follows: In Section 2, a brief review of research related to the proposed approach is given. In Section 3, the sequential pattern mining problem is defined, and the PrefixSpan algorithm is presented. Our proposed approach using PrefixSpan for product placement in supermarket is explained with examples in Section 4 and the experimental results and performance analysis are discussed in Section 5. Section 6 provides the practical implications and future direction of the research. Finally, our approach is concluded in Section 7.

Review of related works
Literature presents a few of research relevant to product placement and shelf allocation problem. Here, we review seven different techniques available in the literature. Brijs et al. (2004) have integrated the discovery of frequent itemsets with a (microeconomic) model for product selection (PROFSET). The model enabled the integration of both quantitative and qualitative (domain knowledge) criteria. Furthermore, they demonstrated that the impact of product Anti-diabetic Compound Library decisions on overall assortment profitability can easily be evaluated by means of sensitivity analysis. On the other hand, Chen et al. (2005) have integrated customer behavioral variables, demographic variables, and transaction database to establish a method of mining changes in customer behavior. The approach for mining changes in customer behavior can assist managers in developing better marketing strategies.
Chen et al. (2006) have used data mining techniques to discover the implicit, meaningful, relationship between the relative spatial distance of displayed products and the items’ unit sales in a retailer’s store. They presented a representation scheme and developed a robust algorithm based on association analysis. To show its efficiency and effectiveness, an intensive experimental study using self-defined simulation data was conducted. Similar to Chen et al. (2006) and Chen and Lin (2007) have utilized a popular data mining approach, association rule mining, instead of space elasticity to resolve the product assortment and allocation problems in retailing. They have applied multi-level association rule mining to explore the relationships between products as well as between product categories.
The specific problem of how to allocate a fixed amount of shelf space to different products within a particular product category was addressed by Reyes and Frazier (2007). A nonlinear integer goal programing formulation was proposed to consider both profitability and customer service factors. The decision support tool was shown anabolic reactions the tradeoffs between increased profitability and improved customer service allowed the manager to make the best tradeoff for the situation. Nafari and Shahrabi (2010) have developed an approach to optimally select and price the products and allocate them to shelf space with consideration of their prices. The paper has taken advantage of data mining techniques, association rules, to find relationships between products regarding their prices. Finally, to show the efficiency and effectiveness of the approach, the experiment on real world data was executed.
Application of data mining techniques in library data results in interesting and useful patterns that can be used to improve services in University libraries. Sitanggang et al. (2010) have presented the results of the work in applying the sequential pattern mining algorithm namely, AprioriAll on a library transaction dataset. Frequent sequential patterns containing book sequences borrowed by students were generated for minimum supports of 0.3, 0.2, 0.15 and 0.1. These patterns helped to develop the library in providing book recommendation to students, conducting book procurement based on readers’ need, as well as managing books’ layout.
By analyzing the above discussed works, the technique given in Brijs et al. (2004) described about selecting the product and the work given in Chen et al. (2005) and Reyes and Frazier, 2007 discussed about developing the marketing strategies using the patterns mined. Importantly, the techniques presented in Chen et al. (2006), Nafari and Shahrabi (2010) and Chen and Lin (2007) are taken for the product allocation problem that usually happened in supermarket. An interesting work was described in Sitanggang et al. (2010) that provided a technique to book recommendation using the rules mined. These works are real motivation of our research in developing the strategy for product placement. Here, we have used the sequential patterns mined from the database for product placement so that the sequence buying behavior will motivate the customers to buy the nearly located products.