Shabanzadeh Parvaneh, Yusof Rubiyah
Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia ; Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia.
Comput Math Methods Med. 2015;2015:802754. doi: 10.1155/2015/802754. Epub 2015 Jul 29.
Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.
无监督数据分类（或聚类）分析是数据挖掘中最有用的工具之一，也是一项描述性任务，旨在根据相似性对同类对象组进行分类，在许多医学学科和各种应用中都有使用。一般来说，没有一种算法适用于所有类型的数据、条件和应用。每种算法都有其自身的优点、局限性和不足之处。因此，对无监督数据分类的新颖有效方法的研究仍在积极进行。本文提出了一种启发式算法——基于生物地理学的优化（BBO）算法，通过修改BBO算法的主要算子，将其应用于数据聚类问题，该算法的灵感来源于不同物种的自然生物地理学分布。与其他基于种群的算法类似，BBO算法从优化问题的候选解初始种群和为其计算的目标函数开始。为了评估所提算法的性能，在六个医学和现实生活数据集上进行了评估，并与八种著名的最新无监督数据分类算法进行了比较。数值结果表明，所提出的进化优化算法对于无监督数据分类是有效的。
