Inspiration Predictive modeling is me-too software that cannot utilize many variables in identifying causal relationships with many variables. Bi-clustering is a data science approach that maintains integrity of many variables by mathematically mapping distance relationships of many variables into a limited variables, predicts or extrapolates out the data, unravels the distance to see those relationships to see how '50' variables are related. For example, chemical processes globally differ even with same approach, what causes failures or downtimes in machines or batch failures? It goes beyond a few sensors to reveal true insight.

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What's next for Multi-variate anomaly detection bi-clustering

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