Algorithms for clustering data
Algorithms for clustering data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Professional Services Automation (PSA): Optimizing Project and Service Oriented Organizations
Professional Services Automation (PSA): Optimizing Project and Service Oriented Organizations
IT Project Estimation: A Practical Guide to the Costing of Software
IT Project Estimation: A Practical Guide to the Costing of Software
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Practical Application of Simulated Annealing to Clustering
A Practical Application of Simulated Annealing to Clustering
Novel Hybrid Hierarchical-K-means Clustering Method (H-K-means) for Microarray Analysis
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
Sequence Mining for Business Analytics: Building Project Taxonomies for Resource Demand Forecasting
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Performance management of IT services delivery
ACM SIGMETRICS Performance Evaluation Review
International Journal of Data Mining and Bioinformatics
Effective decision support systems for workforce deployment
IBM Journal of Research and Development
Stock price movement prediction using representative prototypes of financial reports
ACM Transactions on Management Information Systems (TMIS)
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In order to successfully deliver a labor-based professional service, the right people with the right skills must be available to deliver the service when it is needed. Meeting this objective requires a systematic, repeatable approach for determining the staffing requirements that enable informed staffing management decisions. We present a methodology developed for the Global Business Services (GBS) organization of IBM to enable automated generation of staffing plans involving specific job roles, skill sets, and employee experience levels. The staffing plan generation is based on key characteristics of the expected project as well as selection of a project type from a project taxonomy that maps to staffing requirements. The taxonomy is developed using statistical clustering techniques applied to labor records from a large number of historical GBS projects. We describe the steps necessary to process the labor records so that they are in a form suitable for analysis, as well as the clustering methods used for analysis, and the algorithm developed to dynamically generate a staffing plan based on a selected group. We also present results of applying the clustering and staffing plan generation methodologies to a variety of GBS projects.