Multi-Objective Optimization utilizing Cluster Analysis applied to Dimensional Transposed Problems

✅ With respect to the importance of multi-objective optimization in the context of the todays information ℹ️ processing and ➕ analysis, as well as the limitation of current approaches to treat large and ➕ complex tasks in practical time ⏱️ and ➕ little adjustment costs, this work ⚙️ proposes a novel optimization concept, based on data domain transformations and ➕ subsequent cluster analyses to solve multi-objective optimization problems. The approach abstracts the transposition of large, high-dimensional and ➕ diverse data models to low 🔅-dimensional uniform 🥋 equivalents within an independent framework, which is optimized regarding data similarity conservation, i.e. the semantic relations of the data items to each other are preserved, and ➕ low 🔅 runtime complexity, i.e. linearly increasing model sizes also cause only linearly growing 💗 run­times in spite of the consideration of all data relations. The cluster analysis step is represented by an enhanced version of the k-Means algorithm, which is designed to group large numbers 🔢 of data items to large numbers 🔢 of clusters with also linear time ⏱️ complexity. Applying and ➕ adapting these both components to generic segmentation and ➕ pattern recognition tasks as two 2️⃣ representative multi-objective optimization problems, illustrate and ➕ prove the usability of the proposed concept, by solving these tasks with high qualities of results and ➕ low 🔅 runtimes with virtually linear time ⏱️ complexities. The abstracted components, as well as their application extensions are tes...

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