In this paper we introduce a new ant-based method that takes advantage of the cooperative self-organization of Ant Colony Systems to create a naturally inspired clustering and pattern recognition method. The approach considers each data item as an ant, which moves inside a grid changing the cells it goes through, in a fashion similar to Kohonen's Self-Organizing Maps. The resulting algorithm is conceptually more simple, takes less free parameters than other ant-based clustering algorithms, and, after some parameter tuning, yields very good results on some benchmark problems.
This paper proposes schemes for automated and weighted Self-Organizing Time Maps (SOTMs). The SOTM provides means for a visual approach to evolutionary clustering, which aims at producing a sequence of clustering solutions. This task we denote as visual dynamic clustering. The implication of an automated SOTM is not only a data-driven parametrization of the SOTM, but also the feature of adjusting the training to the characteristics of the data at each time step. The aim of the weighted SOTM is to improve learning from more trustworthy or important data with an instance-varying weight. The sche…
Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achieved through the updating of neuron reference vectors in feature space. Despite the fact that growing variants of SOM overcome the fixed structure limitation they increase computational cost and also do …
Huazhong University of Science and Technology（华中科技大学）
Self-organizing map(SOM) have been widely applied in clustering, this paper focused on centroids of clusters and what they reveal. When the input vectors consists of time, latitude and longitude, the map can be strongly linked to physical world, providing valuable information. Beyond basic clustering, a novel approach to address the temporal element is developed, enabling 3D SOM to track behaviors in multiple periods concurrently. Combined with adaptations targeting to process heterogeneous data relating to distribution in time and space, the paper offers a fresh scope for business and service…
In this study, we synthesize a novel dynamical approach for ant colonies enabling them to migrate to new nest sites in a self-organizing fashion. In other words, we realize ant colony migration as a self-organizing phenotype-level collective behavior. For this purpose, we first segment the edges of the graph of ants' pathways. Then, each segment, attributed to its own pheromone profile, may host an ant. So, multiple ants may occupy an edge at the same time. Thanks to this segment-wise edge formulation, ants have more selection options in the course of their pathway determination, thereby incre…
Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur frequently in real life, for example when data acquisition or labeling is expensive. In this work, we consider the problem of post-labeled few-shot unsupervised learning, a classification task where representations are learned in an unsupervised fashion, to be later labeled using very few annotated examples. We argue that this problem is very likely to occur on the edge, when the embedded device directly acquires th…
In many real world applications, data cannot be accurately represented by vectors. In those situations, one possible solution is to rely on dissimilarity measures that enable sensible comparison between observations. Kohonen's Self-Organizing Map (SOM) has been adapted to data described only through their dissimilarity matrix. This algorithm provides both non linear projection and clustering of non vector data. Unfortunately, the algorithm suffers from a high cost that makes it quite difficult to use with voluminous data sets. In this paper, we propose a new algorithm that provides an importan…
We attack the problem of learning concepts automatically from noisy web image search results. Going beyond low level attributes, such as colour and texture, we explore weakly-labelled datasets for the learning of higher level concepts, such as scene categories. The idea is based on discovering common characteristics shared among subsets of images by posing a method that is able to organise the data while eliminating irrelevant instances. We propose a novel clustering and outlier detection method, namely Rectifying Self Organizing Maps (RSOM). Given an image collection returned for a concept qu…
This work investigates the use of two-stage clustering methods. Four techniques were proposed: SOMK, SOMAK, ASCAK and SOINAK. SOMK is composed of a SOM (Self-Organizing Maps) followed by the K-means algorithm, SOMAK is a combination of SOM followed by the Ant K-means (AK) algorithm, ASCAK is composed by the ASCA (Ant System-based Clustering Algorithm) and AK algorithms, SOINAK is composed by the Self-Organizing Incremental Neural Network (SOINN) and AK. SOINAK presented a better performance among the four proposed techniques when applied to pattern recognition problems.
Universidade Federal de Pernambuco（伯南布哥联邦大学）；Center of Informatics - CIn（信息中心）
Nowadays, with the advance of technology, there is an increasing amount of unstructured data being generated every day. However, it is a painful job to label and organize it. Labeling is an expensive, time-consuming, and difficult task. It is usually done manually, which collaborates with the incorporation of noise and errors to the data. Hence, it is of great importance to developing intelligent models that can benefit from both labeled and unlabeled data. Currently, works on unsupervised and semi-supervised learning are still being overshadowed by the successes of purely supervised learning.…
The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, …
LIPN, Université Sorbonne Paris Nord（索邦巴黎北大学 LIPN 实验室）；Safran Aircraft Engines（赛峰飞机发动机公司）
Self-Organizing Map algorithms have been used for almost 40 years across various application domains such as biology, geology, healthcare, industry and humanities as an interpretable tool to explore, cluster and visualize high-dimensional data sets. In every application, practitioners need to know whether they can \textit{trust} the resulting mapping, and perform model selection to tune algorithm parameters (e.g. the map size). Quantitative evaluation of self-organizing maps (SOM) is a subset of clustering validation, which is a challenging problem as such. Clustering model selection is typica…
South Dakota School of Mines and Technology（南达科他矿业理工学院）
Biologically inspired computing techniques are very effective and useful in many areas of research including data clustering. Ant clustering algorithm is a nature-inspired clustering technique which is extensively studied for over two decades. In this study, we extend the ant clustering algorithm (ACA) to a hybrid ant clustering algorithm (hACA). Specifically, we include a genetic algorithm in standard ACA to extend the hybrid algorithm for better performance. We also introduced novel pick up and drop off rules to speed up the clustering performance. We study the performance of the hACA algori…
This paper introduces the concept of a bi-scale metric for use in the cooperative phase of the self-organizing map (SOM) algorithm. Use of a bi-scale metric allows segmentation of the map into a number of regions, corresponding to anticipated cluster structure in the data. Such a situation occurs, for example, in the somatotopic maps which inspired the SOM algo- rithm, where clusters of data may correspond to body surface regions whose general structure is known. When a bi-scale metric is appropriately applied, issues with map neurons that are not activated by any point in the training data ar…
As introduced by Bentley et al. (2005), artificial immune systems (AIS) are lacking tissue, which is present in one form or another in all living multi-cellular organisms. Some have argued that this concept in the context of AIS brings little novelty to the already saturated field of the immune inspired computational research. This article aims to show that such a component of an AIS has the potential to bring an advantage to a data processing algorithm in terms of data pre-processing, clustering and extraction of features desired by the immune inspired system. The proposed tissue algorithm is…
A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
Department of Core Informatics（核心信息学系）；Osaka Metropolitan University（大阪 metropolitan 大学）；Department of Artificial Intelligence（人工智能系）；Universiti Malaya（马来大学）；Department of Computer Science and Engineering（计算机科学与工程系）；Southern University of Science and Technology（南方科技大学）；Department of Informatics（信息学系）；University of Hamburg（汉堡大学）
The Self-Organizing Map (SOM) with its related extensions is the most popular artificial neural algorithm for use in unsupervised learning, clustering, classification and data visualization. Over 5,000 publications have been reported in the open literature, and many commercial projects employ the SOM as a tool for solving hard real-world problems. Each two years, the "Workshop on Self-Organizing Maps" (WSOM) covers the new developments in the field. The WSOM series of conferences was initiated in 1997 by Prof. Teuvo Kohonen, and has been successfully organized in 1997 and 1999 by the Helsinki …
Sichuan Normal University（四川师范大学）；Shenzhen University（深圳大学）；Chengdu University of Technology（成都理工大学）
Ant Colony Optimization (ACO) has time complexity O(t*m*N*N), and its typical application is to solve Traveling Salesman Problem (TSP), where t, m, and N denotes the iteration number, number of ants, number of cities respectively. Cutting down running time is one of study focuses, and one way is to decrease parameter t and N, especially N. For this focus, the following method is presented in this paper. Firstly, design a novel clustering algorithm named Special Local Clustering algorithm (SLC), then apply it to classify all cities into compact classes, where compact class is the class that all…
Costa Rica Institute of Technology（哥斯达黎加理工学院）；University of Costa Rica（哥斯达黎加大学）
An ant colony optimization approach for partitioning a set of objects is proposed. In order to minimize the intra-variance, or within sum-of-squares, of the partitioned classes, we construct ant-like solutions by a constructive approach that selects objects to be put in a class with a probability that depends on the distance between the object and the centroid of the class (visibility) and the pheromone trail; the latter depends on the class memberships that have been defined along the iterations. The procedure is improved with the application of K-means algorithm in some iterations of the ant…
