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This paper discusses innovative methods for detecting seasonal trends and visualizing cluster motion in very high-dimensional transactional data. It presents findings from G. Gupta and J. Ghosh's research, highlighting techniques such as Value Balanced Agglomerative Connectivity Clustering. The insights gained from these methodologies contribute to advancements in data mining and knowledge discovery, presenting valuable approaches for handling complex datasets in modern applications. The work was presented at notable conferences, including SDM-2001 and the SPIE Conference on Data Mining and Knowledge Discovery in 2001.

• highdimensional data
• seasonal trends
• cluster visualization
• data mining
• agglomerative clustering
• knowledge discovery
• g gupta
• j ghosh

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