Clustering: finding patterns in the darkness

Machine learning is changing the world and fuelling Industry 4.0. These statistical methods focused on identifying patterns in data to provide an intelligent response to specific requests. Although understanding data tends to require expert knowledge to supervise the decision-making process, some techniques need no supervision. These unsupervised techniques can work blindly but they are based on data similarity. One of the most popular areas in this field is clustering. Clustering groups data to guarantee that the clusters’ elements have a strong similarity while the clusters are distinct among them. This field started with the K-means algorithm, one of the most popular algorithms in machine learning with extensive applications. Currently, there are multiple strategies to deal with the clustering problem. This review introduces some of the classical algorithms, focusing significantly on algorithms based on evolutionary computation, and explains some current applications of clustering to large datasets

An Estimation of Distribution Algorithm for Nurse Scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems

Social Network Analysis using Cultural Algorithms and its Variants

Finding relationships between social entities and discovering the underlying structures of networks are fundamental tasks for analyzing social networks. In recent years, various methods have been suggested to study these networks efficiently, however, due to the dynamic and complex nature that these networks have, a lot of open problems still exist in the field. The aim of this research is to propose an integrated computational model to study the structure and behavior of the complex social network. The focus of this research work is on two major classic problems in the field which are called community detection and link prediction. Moreover, a problem of population adaptation through knowledge migration in real-life social systems has been identified to model and study through the proposed method. To the best of our knowledge, this is the first work in the field which is exploring this concept through this approach. In this research, a new adaptive knowledge-based evolutionary framework is defined to investigate the structure of social networks by adopting a multi-population cultural algorithm. The core of the model is designed based on a unique community-oriented approach to estimate the existence of a relationship between social entities in the network. In each evolutionary cycle, the normative knowledge is shaped through the extraction of the topological knowledge from the structure of the network. This source of knowledge is utilized for the various network analysis tasks such as estimating the quality of relation between social entities, related studies regarding the link prediction, population adaption, and knowledge formation. The main contributions of this work can be summarized in introducing a novel method to define, extract and represent different sources of knowledge from a snapshot of a given network to determine the range of the optimal solution, and building a probability matrix to show the quality of relations between pairs of actors in the system. Introducing a new similarity metric, utilizing the prior knowledge in dynamic social network analysis and study the co-evolution of societies in a case of individual migration are another major contributions of this work. According to the obtained results, utilizing the proposed approach in community detection problem can reduce the search space size by 80%. It also can improve the accuracy of the search process in high dense networks by up to 30% compared with the other well-known methods. Addressing the link prediction problem through the proposed approach also can reach the comparable results with other methods and predict the next state of the system with a notably high accuracy. In addition, the obtained results from the study of population adaption through knowledge migration indicate that population with prior knowledge about an environment can adapt themselves to the new environment faster than the ones who do not have this knowledge if the level of changes between the two environments is less than 25%. Therefore, utilizing this approach in dynamic social network analysis can reduce the search time and space significantly (up to above 90%), if the snapshots of the system are taken when the level of changes in the network structure is within 25%. In summary, the experimental results indicate that this knowledge-based approach is capable of exploring the evolution and structure of the network with the high level of accuracy while it improves the performance by reducing the search space and processing time

Pearson coefficient matrix for studying the correlation of community detection scores in multi-objective evolutionary algorithm

Assessing a community detection algorithm is a difficult task due to the absence of finding a standard definition for objective functions to accurately identify the structure of communities in complex networks. Traditional methods generally consider the detecting of community structure as a single objective issue while its optimization generally leads to restrict the solution to a specific property in the community structure. In the last decade, new community detection models have been developed. These are based on multi-objective formulation for the problem, while ensuring that more than one objective (normally two) can be simultaneously optimized to generate a set of non-dominated solutions. However the issue of which objectives should be co-optimized to enhance the efficiency of the algorithm is still an open area of research. In this paper, first we generate a candidate set of partitions by saving the last population that has been generated using single objective evolutionary algorithm (SOEA) and random partitions based on the true partition for a given complex network. We investigate the features of the structure of communities which found by fifteen existing objectives that have been used in literature for discovering communities. Then, we found the correlation between any two objectives using the pearson coefficient matrix. Extensive experiments on four real networks show that some objective functions have a strong correlation and others either neutral or weak correlations