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Recent research works on distributed adaptive networks have intensively studied the case where the nodes estimate a common parameter vector collaboratively. However, there are many applications that are multitask-oriented in the sense that there are multiple parameter vectors that need to be inferred simultaneously. In this paper, we employ diffusion strategies to develop distributed algorithms that address clustered multitask problems by minimizing an appropriate mean-square error criterion with`2-regularization. Some results on the mean-square stability and convergence of the algorithm are also provided. Simulations are conducted to illustrate the theoretical findings.

Interests and applications in data mining are increasing in many businesses. However, this technology is seldom applied to healthcare customer experience management. The paper shows that efficient and effective application of data mining methods can aid the understanding of patient healthcare preferences.

Abstract-In various real-world applications, it is very useful mining unanticipated episodes where certain event patterns unexpectedly lead to outcomes, e.g., taking two medicines together sometimes causing an adverse reaction. These unanticipated episodes are usually unexpected and infrequent, which makes existing data mining techniques, mainly designed to find frequent patterns, ineffective. In this paper, we propose unexpected temporal association rules (UTARs) to describe them. To handle the unexpectedness, we introduce a new interestingness measure, residual-leverage, and develop a novel case-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle the infrequency, we develop a new algorithm MUTARC to find pairwise UTARs. The MUTARC is applied to generate adverse drug reaction (ADR) signals from real-world healthcare administrative databases. It reliably shortlists not only six known ADRs, but also another ADR, flucloxacillin possibly causing hepatitis, which our algorithm designers and experiment runners have not known before the experiments. The MUTARC performs much more effectively than existing techniques. This paper clearly illustrates the great potential along the new direction of ADR signal generation from healthcare administrative databases.

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