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Clustering has been recognized as a very important approach for data analysis that partitions the data according to some (dis)similarity criterion. In recent years, the problem of clustering mixed-type data has attracted many researchers. The k-prototypes algorithm is well known for its scalability in this respect. In this paper, the limitations of dissimilarity coefficient used in the k-prototypes algorithm are discussed with some illustrative examples. We propose a new hybrid dissimilarity coefficient for k-prototypes algorithm, which can be applied to the data with numerical, categorical and mixed attributes. Besides retaining the scalability of the kprototypes algorithm in our method, the dissimilarity functions for either-type attributes are defined on the same scale with respect to their dimensionality, which is very beneficial to improve the efficiency of clustering result. The efficacy of our method is shown by experiments on real and synthetic data sets.

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Disasters are happening due to drastic environmental destructions that may cause damage to wireless data transmission networks. There must be a system that monitors and takes necessary actions for reliable communication which can be provided by the wireless sensor network (WSN) that were organized as multiple nodes. In the heterogeneous environment these ubiquitous nodes are able to handle disasters like floods, drought, earthquake, and cyclone, or network fluctuations through fire accidents. Disasters can be monitored by augmenting a variety of sensors to sense and detect sudden changes in temperature, pressure, seismic wave, noises, etc. Large numbers of sensor nodes are distributed over a geographical area in WSN providing trustable data transfer with multi node sink. In this chapter, the authors review various WSN routing protocols for reliable data transfer in disaster management using multiple sink. The main objective of this chapter is to provide future research directions to enhance QoS in disaster management.

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