Cheng-Fa Tsai

Bio: Cheng-Fa Tsai is an academic researcher from National Pingtung University of Science and Technology. The author has contributed to research in topics: 2-opt & Travelling salesman problem. The author has an hindex of 10, co-authored 15 publications receiving 388 citations.

A new data clustering approach for data mining in large databases

Abstract: Clustering is the unsupervised classification of patterns (data item, feature vectors, or observations) into groups (clusters). Clustering in data mining is very useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric-based similarity measure in order to partition the database such that data points in the same partition are more similar than points in different partitions. In this paper, we present a new data clustering method for data mining in large databases. Our simulation results show that the proposed novel clustering method performs better than a fast self-organizing map (FSOM) combined with the k-means approach (FSOM+k-means) and the genetic k-means algorithm (GKA). In addition, in all the cases we studied, our method produces much smaller errors than both the FSOM+k-means approach and GKA.

A new approach for solving large traveling salesman problem using evolutionary ant rules

Abstract: This paper presents a new metaheuristic method called EA algorithm for solving the TSP (traveling salesman problem). We introduce a genetic exploitation mechanism in ant colony system from genetic algorithm to search solutions space for solving the traveling salesman problem. In addition, we present a method called nearest neighbor (NN) to EA to improve TSPs thus obtain good solutions quickly. According to our simulation results, the EA algorithm outperforms the ant colony system (ACS) in tour length comparison of traveling salesman problem. In this work it is observed that EA or ACS with NN approach as initial solutions can provide a significant improvement for obtaining a global optimum solution or a near global optimum solution in large TSPs.

MSGKA: an efficient clustering algorithm for large databases

Abstract: This investigation presents an efficient clustering algorithm for large databases. We present a novel multiple-searching genetic algorithm (MSGA) that finds a globally optimal partition of a given data into a specified number of clusters. We hybridize MSGA with a multiple-searching approach utilized in clustering namely, K-means algorithm. Hence, the name multiple-searching genetic K-means algorithm (MSGKA). Our simulation results reveal that the proposed novel clustering approach performs better than the Fast SOM combines K-means approach (FSOM+K-means) and Genetic K-Means Algorithm (GKA). Moreover, in all the cases we studied, our approach produces much smaller errors than both the FSOM+K-means and GKA.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem

Abstract: The Traveling Salesman Problem (TSP) is one of the standard test problems used in performance analysis of discrete optimization algorithms. The Ant Colony Optimization (ACO) algorithm appears among heuristic algorithms used for solving discrete optimization problems. In this study, a new hybrid method is proposed to optimize parameters that affect performance of the ACO algorithm using Particle Swarm Optimization (PSO). In addition, 3-Opt heuristic method is added to proposed method in order to improve local solutions. The PSO algorithm is used for detecting optimum values of parameters α and β which are used for city selection operations in the ACO algorithm and determines significance of inter-city pheromone and distances. The 3-Opt algorithm is used for the purpose of improving city selection operations, which could not be improved due to falling in local minimums by the ACO algorithm. The performance of proposed hybrid method is investigated on ten different benchmark problems taken from literature and it is compared to the performance of some well-known algorithms. Experimental results show that the performance of proposed method by using fewer ants than the number of cities for the TSPs is better than the performance of compared methods in most cases in terms of solution quality and robustness.

Swarm Intelligence in Data Mining

Abstract: Devoted to novel optical measurement techniques that are applied both in industry and life sciences, this book contributes a fresh perspective on the development of modern optical sensors. These sensors are often essential in detecting and controlling parameters that are important for both industrial and biomedical applications. The book provides easy access for beginners wishing to gain familiarity with the innovations of modern optics.

Editorial survey: swarm intelligence for data mining

Abstract: This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and data mining. Whereas data mining has been a popular academic topic for decades, swarm intelligence is a relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flocks of birds, fish schools and bee hives, where a number of individuals with limited capabilities are able to come to intelligent solutions for complex problems. In recent years the swarm intelligence paradigm has received widespread attention in research, mainly as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). These are also the most popular swarm intelligence metaheuristics for data mining. In addition to an overview of these nature inspired computing methodologies, we discuss popular data mining techniques based on these principles and schematically list the main differences in our literature tables. Further, we provide a unifying framework that categorizes the swarm intelligence based data mining algorithms into two approaches: effective search and data organizing. Finally, we list interesting issues for future research, hereby identifying methodological gaps in current research as well as mapping opportunities provided by swarm intelligence to current challenges within data mining research.

Clustering Algorithms Research

Abstract: The research actuality and new progress in clustering algorithm in recent years are summarized in this paper First, the analysis and induction of some representative clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage On the other hand, several typical clustering algorithms and known data sets are selected, simulation experiments are implemented from both sides of accuracy and running efficiency, and clustering condition of one algorithm with different data sets is analyzed by comparing with the same clustering of the data set under different algorithms Finally, the research hotspot, difficulty, shortage of the data clustering and some pending problems are addressed by the integration of the aforementioned two aspects information The above work can give a valuable reference for data clustering and data mining