بایگانی برچسب برای: Clustering

The planar hub location problem a probabilistic.[taliem.ir]

The planar hub location problem: a probabilistic clustering approach

Given the demand between each origin-destination pair on a network, the planar hub location problem is to locate the multiple hubs anywhere on the plane and to assign the traffic to them so as to minimize the total travelling cost. The trips between any two points can be nonstop (no hubs used) or started by visiting any of the hubs. The travel cost between hubs is discounted with a factor. It is assumed that each point can be served by multiple hubs. We propose a probabilistic clustering method for the planar hub-location problem which is analogous to the method of Iyigun and Ben-Israel (in Operations Research Letters 38, 207–214, 2010; Computational Optmization and Applications, 2013) for the solution of the multi-facility location problem. The proposed method is an iterative probabilistic approach assuming that all trips can be taken with probabilities that depend on the travel costs based on the hub locations. Each hub location is the convex combination of all data points and other hubs. The probabilities are updated at each iteration together with the hub locations. Computations stop when the hub locations stop moving. Fermat-Weber problem and multi-facility location problem are the special cases of the proposed approach.
Integrating AHP and data mining for product recommendation.[taliem.ir]

Integrating AHP and data mining for product recommendation based on customer lifetime value

Product recommendation is a business activity that is critical in attracting customers. Accordingly, improving the quality of a recommendation to fulfill customers’ needs is important in fiercely competitive environments. Although various recommender systems have been proposed, few have addressed the lifetime value of a customer to a firm. Generally, customer lifetime value (CLV) is evaluated in terms of recency, frequency, monetary (RFM) variables. However, the relative importance among them varies with the characteristics of the product and industry. We developed a novel product recommendation methodology that combined group decision-making and data mining techniques. The analytic hierarchy process (AHP) was applied to determine the relative weights of RFM variables in evaluating customer lifetime value or loyalty. Clustering techniques were then employed to group customers according to the weighted RFM value. Finally, an association rule mining approach was implemented to provide product recommendations to each customer group. The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a typical collaborative filtering (CF) method.
A Cluster Based Multi-Radio Multi-Channel Assignment[taliem.ir]

A Cluster Based Multi-Radio Multi-Channel Assignment Approach in Wireless Mesh Networks

Wireless mesh networks (WMNs) are receiving increasing attention as an effective means to provide broadband internet. Throughput is a major QoS in WMNs keeping in view of their perceived application areas and others being connectivity and reliability. WMNs use multiple radios and orthogonal communication channels to reduce interference and increase throughput and at the same time providing path redundancy, reliability and connectivity. In our work we propose a cluster based channel assignment scheme for WMNs and assume that the wireless radio interfaces are equipped with IEEE 802.11 network interface cards (NICs). We have extensively simulated our work using ns-2 network simulator and compared it against well-known channel assignment techniques and our result exhibits a significant increase in throughput.
Selection criteria for text mining approaches[taliem.ir]

Selection criteria for text mining approaches

Text mining techniques include categorization of text, summarization, topic detection, concept extraction, search and retrieval, document clustering, etc. Each of these techniques can be used in finding some non- trivial information from a collection of documents. Text mining can also be employed to detect a document’s main topic/theme which is useful in creating taxonomy from the document collection. Areas of applications for text mining include publishing, media, telecommunications, marketing, research, healthcare, medicine, etc. Text mining has also been applied on many applications on the World Wide Web for developing recommendation systems. We propose here a set of criteria to evaluate the effectiveness of text mining techniques in an attempt to facilitate the selection of appropriate technique.
Mining customer knowledge for tourism new product development and[taliem.ir]

Mining customer knowledge for tourism new product development and customer relationship management

In recent years tourism has become one of the fastest growing sectors of the world economy and is widely recognized for its contribution to regional and national economic development.