Extraction of User Navigation Pattern Based on Particle Swarm Optimization

With current projections regarding the growth of Internet sales, online retailing raises many questions about how to market on the Net. A Recommender System (RS) is a composition of software tools that provides valuable piece of advice for items or services chosen by a user. Recommender systems are currently useful in both the research and in the commercial areas. Recommender systems are a means of personalizing a site and a solution to the customer?s information overload problem. Recommender Systems (RS) are software tools and techniques providing suggestions for items and/or services to be of use to a user. These systems are achieving widespread success in e-commerce applications nowadays, with the advent of internet. This paper presents a categorical review of the field of recommender systems and describes the state-of-the-art of the recommendation methods that are usually classified into four categories: Content based Collaborative, Demographic and Hybrid systems. To build our recommender system we will use fuzzy logic and Markov chain algorithm

Development of a R package to facilitate the learning of clustering techniques

This project explores the development of a tool, in the form of a R package, to ease the process of learning clustering techniques, how they work and what their pros and cons are. This tool should provide implementations for several different clustering techniques with explanations in order to allow the student to get familiar with the characteristics of each algorithm by testing them against several different datasets while deepening their understanding of them through the explanations. Additionally, these explanations should adapt to the input data, making the tool not only adept for self-regulated learning but for teaching too.Grado en Ingeniería Informátic

A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels

In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows to construct Mamdani fuzzy models considering both accuracy (precision) and transparency (interpretability) of fuzzy systems. The new methodology employs a fast hierarchical clustering algorithm to generate an initial fuzzy model efficiently; a training data selection mechanism is developed to identify appropriate and efficient data as learning samples; a high-performance Particle Swarm Optimisation (PSO) based multi-objective optimisation mechanism is developed to further improve the fuzzy model in terms of both the structure and the parameters; and a new tolerance analysis method is proposed to derive the confidence bands relating to the final elicited models. This proposed modelling approach is evaluated using two benchmark problems and is shown to outperform other modelling approaches. Furthermore, the proposed approach is successfully applied to complex high-dimensional modelling problems for manufacturing of alloy steels, using ‘real’ industrial data. These problems concern the prediction of the mechanical properties of alloy steels by correlating them with the heat treatment process conditions as well as the weight percentages of the chemical compositions

The Improved K-Means with Particle Swarm Optimization

In today’s world data mining has become a large field of research. As the time increases a large amount of data is accumulated. Clustering is an important data mining task and has been used extensively by a number of researchers for different application areas such as finding similarities in images, text data and bio-informatics data. Cluster analysis is one of the primary data analysis methods. Clustering defines as the process of organizing data objects into a set of disjoint classes called clusters. Clustering is an example of unsupervised classification. In clustering, K-Means (Macqueen) is one of the most well known popular clustering algorithm. K-Means is a partitioning algorithm follows some drawbacks: number of clusters k must be known in advanced, it is sensitive to random selection of initial cluster centre, and it is sensitive to outliers. In this paper, we tried to improve some drawbacks of K-Means algorithm and an efficient algorithm is proposed to enhance the K-Means clustering with Particle Swarm Optimization. In recent years, Particle Swarm Optimization (PSO) has been successfully applied to a number of real world clustering problems with the fast convergence and the effectively for high-dimensional data. Keywords: Clustering, K-Means clustering, PSO (Particle Swarm Optimization), Hierarchical clustering.

Document clustering for knowledge discovery using nature-inspired algorithm

As the internet is overload with information, various knowledge based systems are now equipped with data analytics features that facilitate knowledge discovery.This includes the utilization of optimization algorithms that mimics the behavior of insects or animals.This paper presents an experiment on document clustering utilizing the Gravitation Firefly algorithm (GFA).The advantage of GFA is that clustering can be performed without a pre-defined value of k clusters.GFA determines the center of clusters by identifying documents with high force.Upon identification of the centers, clusters are created based on cosine similarity measurement.Experimental results demonstrated that GFA utilizing a random positioning of documents outperforms existing clustering algorithm such as Particles Swarm Optimization (PSO) and K-means

Fuzzy adaptive resonance theory: Applications and extensions

Adaptive Resonance Theory, ART, is a powerful clustering tool for learning arbitrary patterns in a self-organizing manner. In this research, two papers are presented that examine the extensibility and applications of ART. The first paper examines a means to boost ART performance by assigning each cluster a vigilance value, instead of a single value for the whole ART module. A Particle Swarm Optimization technique is used to search for desirable vigilance values. In the second paper, it is shown how ART, and clustering in general, can be a useful tool in preprocessing time series data. Clustering quantization attempts to meaningfully group data for preprocessing purposes, and improves results over the absence of quantization with statistical significance. --Abstract, page iv

A Swarm Based Approach to Improve Traditional Document Clustering Approach

Clustering, an extremely important technique in Data Mining is an automatic learning technique aimed at grouping a set of objects into subsets or clusters. The goal is to create clusters that are coherent internally, but substantially different from each other. Text Document Clustering refers to the clustering of related text documents into groups based upon their content. Document clustering is a fundamental operation used in unsupervised document organization, text data mining, automatic topic extraction, and information retrieval. Fast and high - quality document clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. The documents to be clustered can be web news articles, abstracts of research papers etc. The aim of this paper is to provide efficient document clustering technique involving the application of soft computing approach and the use of swarm intelligence based algorithm