Popularity estimation of interesting locations from visitor’s trajectories using fuzzy inference system

Identifying the interesting places through GPS trajectory mining has been well studied based on the visitor’s frequency. However, the places popularity estimation based on the trajectory analysis has not been explored yet. The limitation in the majority of the traditional popularity estimation and place user-rating based methods is that all the participants are given the same importance. In reality, it heavily depends on the visitor’s category, for example, international visitors make distinct impact on popularity. The proposed method maintains a registry to keep the information about the visited users, their stay time and the travel distance from their home location. Depending on the travel nature the visitors are labeled as native, regional and tourist for each place in question. It considers the fact that the higher stay in a place is an implicit measure of the greater likings. Theweighted frequency is eventually fuzzified and applied rule based fuzzy inference system (FIS) to compute popularity of the places in terms of the ratings ∈ [0, 5]. We have evaluated the proposed method using a large real road GPS trajectory of 182 users for identifying the ratings for the collected 26807 point of interests (POI) in Beijing (China)

Restricting the size of Case Base(for auctions) using Genetic Algorithms

The advent of e-commerce has brought about a radical change in the process of auctions. There is a trend towards automation in the process of auctions. Agents are widely used in this automation process. Casebased reasoning is a process which provides a user a method to automate the process of bidding. This process has an inherent learning component. CBR combined with a bidding agent for strategizing the bidding functionality provides a flexible solution for automating the bidding process for any type of auction in any domain. This paper proposes the use of Genetic Algorithms to restrict the size of the structure of Case Base. This is achieved by optimizing the distributed rules in the case base. Keywords: Learning; Case-based reasoning; E-Commerce; Agents; Genetic Algorithm