., Rakesh Ranjan (2013) *K-Nearest Leader Follower Classifier.* BTech thesis.

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## Abstract

In pattern recognition, the k-nearest neighbor classiﬁer (k-NN) is a non-parametric approach for classifying test cases based on closest training set elements in the given dataset. k-NN belongs to the category of instance-based learning, or lazy learning where the function is estimated locally and all computation is delayed until classi- ﬁcation. The k-nearest neighbor algorithm is supposed to be easiest of all machine learning algorithms, in which an object is classiﬁed by a bulk vote of its neighbors, with the object being allotted to the class most common amongst its k nearest neigh- bors. If k = 1, then the object is merely assigned to the class of its nearest neighbor. Nearest Neighbor classiﬁer and its variants like k-Nearest Neighbor (KNN) classiﬁer are popular because of their simplicity and good performance. But one of the major limitations of KNN is that it has to search the entire training set in order to classify a given pattern which proves to be very expensive when a big sized training set is given or dimension of training set is high. In this paper, a generalized k-nearest leader follower classiﬁcation algorithm is presented, which is an improvement over k-nearest neighbor rule. It can ﬁnd the class of a test dataset in less time with respect to tra- ditional Nearest-neighbor and KNN. The method is to ﬁnd a set of leaders with the concept of clustering using a pre- deﬁned minimum distance between two leaders as tou. After ﬁnding leaders set, rest of the remaining training set is classiﬁed into follower set, with a group of followers assigned to each leader. Now the k-nearest leaders will be found out of the obtained leader set by ﬁnding the distance of a given test set from the leader set. And ﬁnally using these k-nearest leaders and their corresponding followers, class of the dataset is found. The algorithm is empirically tested with some standard data sets and a comparison is made with some of the earlier classiﬁers.

Item Type: | Thesis (BTech) |
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Uncontrolled Keywords: | KNN, nearest neighbor, k-nearest, data mining, classifier, clustering, weighted, leader, follower |

Subjects: | Engineering and Technology > Computer and Information Science > Data Mining |

Divisions: | Engineering and Technology > Department of Computer Science |

ID Code: | 5186 |

Deposited By: | Hemanta Biswal |

Deposited On: | 10 Dec 2013 16:44 |

Last Modified: | 10 Dec 2013 16:44 |

Supervisor(s): | Patra, B K |

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