A Survey of Mining of Complex Data Using Combined Mining Approach

Combined mining as a general approach to mining for informative patterns which combine elements from either multiple information sets method or multiple options or by multiple ways on demand. It is a general methodology for directly distinctive patterns which introduce constituents from multiple sources or with heterogeneous options like covering enumeration, performance, and business money. In certain situations, a single method or one-step mining is often limited in discovering informative knowledge. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple compatible business lines, provisioning line of work for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more instructive patterns, e.g. combining frequent pattern mining with classifications to generate frequent pattern-based classifiers. Rather than extending a specific algorithm, this paper builds our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on demand. We summarize extensive frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Different types of combined patterns, It can be such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been conducted to test the frameworks, with some of them briefed in this paper.