Amalanathan Geetha Mary
Bio: Amalanathan Geetha Mary is an academic researcher from VIT University. The author has contributed to research in topics: Universal set & Rough set. The author has an hindex of 1, co-authored 2 publications receiving 2 citations.
••01 Jan 2019
TL;DR: By using intuitionistic fuzzy soft set relation, an idea for detecting outliers in two universal sets in which the attribute-based and object-based weighted density values are determined to detect outliers is proposed.
Abstract: Data mining is the process of examining large databases to extract patterns, knowledge and to establish relationships to provide solution for the complex problems. Researchers have shown their interest in different areas particularly in identifying outliers. An object which significantly deviates from other objects or by their normal behaviour is termed as outliers. Real-world data have vagueness and uncertainty which can be handled by rough set theory. Research works that have been carried so far were focussed only on single universal set to detect outliers. By using intuitionistic fuzzy soft set relation, this paper proposes an idea for detecting outliers in two universal sets in which the attribute-based and object-based weighted density values are determined to detect outliers. The hiring dataset has taken for example and shown the validity of the proposed approach for outlier detection.
••01 Jan 2019
TL;DR: Several features to identify a phishing site are discussed, using data mining techniques like classification and association rule mining many explorations are performed to prove the notion.
Abstract: With the growth in the present digital era, the Internet is the prime source of knowledge. This situation is depleted by phishers and they have drafted various websites which steals user’s information and misuse it. Though it is hard to locate a phishing site, various features of the phishing site helps in uncovering its mask. This paper discusses several features to identify a phishing site. Using data mining techniques like classification and association rule mining many explorations are performed to prove the notion. Similarly, the impact of various features considered for analysis is studied too.
TL;DR: The weighted density outlier detection method based on rough entropy calculates weights of each object and attribute and the threshold value is fixed to determine outliers, which provides a solution for both incomplete and indeterminate information.
01 Jul 2020
TL;DR: The main goal of this article is to detect outliers significant in two universal sets by applying the intuitionistic fuzzy cut relationship based on membership and non-membership values, and to prove its efficiency.
Abstract: The process of recognizing patterns, collecting knowledge from massive databases is called data mining. An object which does not obey and deviates from other objects by their characteristics or behavior are known as outliers. Research works carried out so far on outlier detection were focused only on numerical data, categorical data, and in single universal sets. The main goal of this article is to detect outliers significant in two universal sets by applying the intuitionistic fuzzy cut relationship based on membership and non-membership values. The proposed method, weighted density outlier detection, is based on rough entropy, and is employed to detect outliers. Since it is unsupervised, without considering class labels of decision attributes, weighted density values for all conditional attributes and objects are calculated to detect outliers. For experimental analysis, the Iris dataset from the UCI repository is taken to detect outliers, and comparisons have been made with existing algorithms to prove its efficiency.