Data Mining and Knowledge Engineering

Ajaya K. Akaspau ¹, P. Srinivasa Rao ², Lokesh K. Sharma ³

Affiliations
1 Rungta College of Engineering and Technology, Bhilai (CG), IN
2 Department of Computer Science and Systems Engineering, Andhra University, Visakhapatnam, IN
3 Department of Computer Science and Engineering, Rungta College of Engineering and Technology, Bhilai (CG), IN

Abstract

Trajectory data mining is an emerging area of research, having a large variety of applications. This paper proposes an efficient nearest neighbour based trajectory data classification. The nearest neighbour classification is simplest method. The main issue of a Nearest Neighbour classifier is measuring the distance between two items, and this becomes more complicated for Trajectory Data. The closeness between objects is determined using a distance measure. Despite its simplicity, Nearest Neighbour also has some drawbacks: 1) it suffers from expensive computational cost in training when the training set contains millions of objects; 2) its classification time is linear to the size of the training set. The larger the training set, the longer it takes to search for the nearest neighbors. To improve the efficiency of algorithm an R-tree data structure is used. Extensive experiments were conducted using real datasets of moving vehicles in Milan (Italy) and London (UK). Our experimental investigation yields output as classified test trajectories, significant in terms of correctly classified success rate being 98.2%, the results are discussed with the summaries of confusion matrix. To measure the agreement between predicted and observed categorization of the dataset is carried out using Kappa statistics.

Keywords

Trajectory Data Mining, Trajectory Classification, Mobility Data, Nearest Neighbour.

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K. Srinivasa Rao ¹, K. Kiran Kumar ¹, P. Srinivasa Rao ¹

Affiliations
1 Department of Information Technology, Bapatla Engineering College, Bapatla, Andhra Pradesh, IN

Abstract

Cluster analysis is one of the major data analysis methods for clustering the large data sets. The cluster analysis deals with the problems of organization of a collection of data objects into clusters based on some similarity. K-means is one of the most popular data partitioning algorithms that solve the well known clustering problem. Performance of the k-means clustering greatly depends upon the correctness of the initial centroids. Typically the initial centroids for the original k-means clustering are determined randomly. So, the clustering result may reach the local optimal solutions, not the global optimum. Several improvements have been proposed to improve the performance of k-means algorithm. This paper proposes an Efficient k-means algorithm for finding the better initial centroids and an efficient way for assigning data points to appropriate clusters. The proposed algorithm is tested with six bench mark datasets, which are taken from UCI machine learning data repository and found that the proposed algorithm gives better result than the existing.

Keywords

Clustering, Data Partitioning, Data Mining, Heuristic K-Means, K-Means Algorithm.

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