SFLOSCAN: A Biologically Inspired Data Mining Framework for Community Identification in Dynamic Social Networks

Predictive Analytics and Its Relationship with Prognostic and Health Management of Machinery 

Anticipation of the needs for machinery maintenance and replacement is of great value to manufacturers of all types and many other businesses. Hence, the field of Prognostic and Health Management (PHM) developed over the past few decades . The PHM Society serves such work:www.phmsociety.org. Predictive Analytics (PA) has emerged in recent years [2,3], and has many similarities to PHM. The developed field of PHM and the newer field of PA share common methodologies and goals. The purpose of this study is to examine the similarities and differences between PHM of machinery and PA.

Nature Inspired Predictive Analytics

Community Detection techniques are key in building predictive analytics model for social analytics, customer behavior analytics and fraud detection. Discovering a community in a social network is to detect a cluster of social members whose rate of interactions among those members is higher than of those outside of the social cluster. This talk will survey and highlight the present state of the art algorithms that are widely used in this space with a specific focus on biologically inspired techniques (Clustering Algorithms inspired from the natural bird flocking behavior - FlockbyLeader algorithm for community detection, and ant colonies for recommender systems) It is important to note that most of the major vendors of predictive analytics do not include biologically inspired algorithms in their suite of algorithms.

BIOIKDD: Biologically Inspired Knowledge Discovery       

Biologically inspired knowledge discovery techniques have recently received significant attention. It is one of the most important tools for complex systems and problems evaluation. They have been used in several research areas such as collaborative filtering, clustering, classification, and other areas of data mining. These techniques are depicted in nature of swarms of birds, fish, ants, and other land animals. In recent years, we observed there is a huge increase in the number of papers published in such area. The aim of the workshop is to highlight the current research related to biologically inspired knowledge discovery techniques in different data mining domains and their applications not limited to Social Networks Analysis, Text Mining, and Bioinformatics.