Inclusive Analysis of Incomplete Datasets Using 1Knn Search
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2017-08-18T10:08:30Z
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Abstract
Analyzing and processing any dataset is very important for any organization as it helps in making
key business decisions of a organization and also increases the profit of any business organization.
But these data sets also include incomplete data sets which are often eliminated in the preprocessing
techniques. Here, incompleteness refers to the case where data has error or certain
information is missing. In this project we focus only on missing data. Missing data exists due to
failure of data transmission devices, accidental loss of data or improper storage. Although we can
simply perform all the analysis tasks based on complete data sets by removing all the incomplete
data, the analysis in incomplete and output is inaccurate. Given a dataset of multi-dimensional
objects and a query object, finding k closest objects to the query from the dataset without
discarding the incomplete data records (IkNN query) is a fundamental problem in data mining.
This concept has its significant role in real time applications like image recognition, location based
services, etc. Our objective of this project is to develop and present efficient indexes, pruning
techniques and algorithms to support the execution of IkNN queries efficiently.
We develop a front end for the data analyst to query the data set using java swing. The java code
acts as a client to Rserve which is the backend server in our project. The Rserve accepts the query
and executes the search algorithm and provides the k nearest neighbor object of the query to the
data analyst. The algorithm uses LαB index, α-pruning and Distance pruning techniques to perform
the search efficiently.
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Inclusive Analysis of Incomplete Datasets Using 1Knn Search, 1NH13IS108, Sujithra K S, Supriya P