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Abstract: Density-based clustering methods extract high density clusters which are separated by regions of lower density. HDBSCAN* is an existing algorithm for producing a density-based cluster hierarchy. To obtain clusters from this hierarchy it includes an instance of FOSC(Framework for Optimal Selection of Clusters) to extract significant clusters, based on a measure known as cluster stability. We introduce CASAR (Compact And Separation Adjusted Ratio), a new algorithm for extracting significant clusters from an HDBSCAN* hierarchy. CASAR issimilar to FOSC, but defines local cluster quality differently and also uses a different aggregation method for comparing the quality of descendant clusters to ancestors in the hierarchy. The local cluster quality that CASAR uses is based on the validation index DBCV (Density-Based Cluster Validation). CASAR is designed to extract individual density-based clusters from subspaces, and is not meant to be a general purpose replacement for cluster stability. We also introduce a new semi-supervised density-based method for finding relevant subspaces. Given a set of should-link objects that belong to an undiscovered cluster, our method finds an appropriate set of attributes for extracting the cluster. Our method makes use of well-established qualities of density-based clusters, and as such, it can be used as a pre-processing step for a wide variety of different density-based clustering algorithms. We combine this method with HDBSCAN* and CASAR to produce a semi-supervised density-based projected clustering algorithm. In a series of experiments, we compare CASAR and cluster stability on both synthetic data and on real data sets. We also compare our semi-supervised density-based projected clustering algorithm to an existing semi-supervised projected clustering algorithm and to a well-known unsupervised projected clustering algorithm. We conclude this thesis with a summary of the strengths and weaknesses of our method, a summary of experimental findings, and a discussion about possible directions for future work.

Abstract: Improper management of labour resources causes major problems for companies working on multiple industrial construction projects. To address these problems, an integrated framework is developed based on a five-step knowledge discovery in data model. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. First, a synthesis of previous research is presented. Second, an inclusive analysis of the industrial construction domain is performed. Third, the concept of predefined progressable work packages is introduced to address issues in current data management practices. Fourth, a prototype data warehouse is built using the snowflake schema to centrally store the data, produce dynamic reports and exchange knowledge. Fifth, data mining techniques are applied to extract useful knowledge from three sets of real projects data. Results show that the developed framework is capable of transferring previously unanalyzed data to valuable knowledge that significantly improves current resources management practices.