Clustering temporal data compiled from cancer registries is a crucial problem faced by many data analyst owing to the elevated high dimensionality, weight value calculation, multi view data and multifaceted temporal correlation. This research work reveals a hypothetical effect of Temporal Clustering (TC) in various domains on cancer genome risk estimates by introducing data mining clustering algorithms. For the first time, cancer genome datasets samples were made available for the complete genome sequences consisting of point mutations and structural alternations for a huge number of cancer types which allows the variation of cancer subtypes in an exceptional excellent global analysis. In this work, TC algorithm is presented to the allocation of several time- series into a set of non-overlapping parts that fit in to k temporal clusters. The paper presents a group of clustering communal framework for multi view data, TW-K-means and an automated two-level variable clustering algorithm that can be used to calculate the weights for views and person variables. A new ATBCWCE structure is projected to improve the risk estimates in cancer genome.

Temporal data clustering, weighted consensus function, multi view learning, k-means

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