Intricacies of Unstructured Data

Intricacies of Unstructured Data

This research paper is an attempt to explore unstructured data that has taken the world of data science by storm. Handling the unprecedented growth of unstructured data is a big challenge faced by most data scientists. This paper discusses about the types and sources of unstructured data and effective ways of processing and analysing this data.

Nonparametric Regression Estimation under Kernel Polynomial Model for Unstructured Data

The nonparametric estimation(NE) of kernel polynomial regression (KPR) model is a powerful tool to visually depict the effect of covariates on response variable, when there exist unstructured and heterogeneous data. In this paper we introduce KPR model that is the mixture of nonparametric regression models with bootstrap algorithm, which is considered in a heterogeneous and unstructured framewo...

Intricacies of Pluripotency

Pluripotent stem cells have the potential to differentiate into 200 odd cell types present in adult body. Pluripotent stem cells available for regenerative medicine include embryonic stem (ES) cells, induced pluripotent stem (iPS) cells and very small ES-like stem (VSELs) cells. Nuclear OCT-4 is one of the crucial factors that dictate pluripotent state. Compared to ES/iPS cells grown in Petri d...

Patterns in Unstructured Data

Anonymizing Unstructured Data

In this paper we consider the problem of anonymizing datasets in which each individual is associated with a set of items that constitute private information about the individual. Illustrative datasets include market-basket datasets and search engine query logs. We formalize the notion of k-anonymity for set-valued data as a variant of the k-anonymity model for traditional relational datasets. W...