Survey on Inclusive Analysis of Incomplete Datasheets

Analyzing and processing any dataset is very important for any organization as it helps in making key business decisions of an organization and also increases the profit of any business organization. However, these data sets also include incomplete data sets, which are often eliminated in the pre-processing techniques. Incompleteness is the common problem that most of the datasets suffer from. The incompleteness refers to any missing or uncertain data in the datasets. The missing data exists due to failure of data transmission devices, accidental loss of data or improper storage. Given a dataset of multi-dimensional objects and a query object, finding k closest objects to the query from the dataset without eliminating the missing value data object is a fundamental problem in data mining. This concept has a significant role in real time applications like image recognition, location based services, etc. In this paper, we study how to retrieve k-closest object to a given query from datasets with incomplete data. Further, we explain and discuss the latest techniques used to improve the accuracy of such data retrieval. We then analyze and compare the results obtained, efficiency and performance of all the techniques discussed.