![]() ![]() We find evidence that design-based multi-start can be more efficient as the size of databases grow large. Importantly, we perform experiments to compare two alternative methods of re-starting the search for the former heuristic, specifically a random-sampling multi-start and a deterministic design-based multi-start. We perform computational experiments to compare performance of recent heuristic, the very large-scale neighborhood search, with a Greedy algorithm, another heuristic for the MAP, as well as with two versions of genetic algorithm, a general metaheuristic. We evaluate and compare the performance of these algorithms and their modifications on synthetically generated data. Because the optimization problem is NP-hard, we apply two heuristic procedures, a Greedy algorithm and very large scale neighborhood search, to solve the assignment problem and find the most likely matching of records from multiple datasets into a single entity. ![]() As a motivation for our approach, we illustrate the advantage of multipartite entity resolution over sequential bipartite matching. We derive a mathematical formulation for a general class of record linkage problems in multipartite entity resolution across many datasets as a combinatorial optimization problem known as the multidimensional assignment problem. Multipartite entity resolution aims at integrating records from multiple datasets into one entity. The proposed methodology can be used to analyze time-dependent patterns and other immigration data for different countries as well. The findings obtained from this study can be a basis for developing policies and strategies that facilitate the labor market integration of the immigrants. The results reveal the profiles of Syrian refugees with work permit applications. In the fourth phase, the association rules are generated to reveal the interesting and frequent properties of each cluster. In the third phase, decision tree is used to specify the distinguishing characteristics of the clusters. Self-organizing map and hierarchical clustering are implemented for this purpose. In the second phase, the profiles of the Syrian refugee workers are determined using clustering. In the first phase, data pre-processing and visualization operations are performed. clustering, classification, and association rule mining, and it has four phases. The proposed approach integrates several data mining tasks, i.e. The proposed methodology aims to extract the hidden, interesting and useful characteristics of the Syrian refugees having formal employment potential. The dataset includes demographic properties of the applicants and characteristics of their workplaces. In this coes with work permit alications are examined between years 20. Motivated by this, we focus on the formal employment of Syrian refugees in Turkey, and propose a data mining based methodology in order to understand their profiles. With the technological advancements in data collection systems, data-driven approaches become a necessity for understanding and managing the socioeconomic systems.
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