An Attempt to Find Neighbors

In this paper, we present our continuous research on similarity search problems. Previously we proposed PanKNN[18]which is a novel technique that explores the meaning of K nearest neighbors from a new perspective, redefines the distances between data points and a given query point Q, and efficiently and effectively selects data points which are closest to Q. It can be applied in various data mining fields. In this paper, we present our approach to solving the similarity search problem in the presence of obstacles. We apply the concept of obstacle points and process the similarity search problems in a different way. This approach can assist to improve the performance of existing data analysis approaches

An Approach to Nearest Neighboring Search for Multi-dimensional Data

Finding nearest neighbors in large multi-dimensional data has always been one of the research interests in data mining field. In this paper, we present our continuous research on similarity search problems. Previously we have worked on exploring the meaning of K nearest neighbors from a new perspective in PanKNN [20]. It redefines the distances between data points and a given query point Q, efficiently and effectively selecting data points which are closest to Q. It can be applied in various data mining fields. A large amount of real data sets have irrelevant or obstacle information which greatly affects the effectiveness and efficiency of finding nearest neighbors for a given query data point. In this paper, we present our approach to solving the similarity search problem in the presence of obstacles. We apply the concept of obstacle points and process the similarity search problems in a different way. This approach can assist to improve the performance of existing data analysis approaches

Effect of Physical Constraints on Spatial Connectivity in Urban Areas

Obstacle effect on proximity, connectivity, and organization of spatial data calls for derivation of measures that enable quantifying their influence. Provision of such measures is valuable for ensuring an aware planning, analysis of obstacle impact on spatial data, and the consequent placement of crossings. This paper proposes quantifying obstacle influence via their impact on connectivity and aggregation of data. As the paper shows, the derived indices enable capturing the actual obstacle effect on spatial data while accommodating datasets with different level of complexity. The information and contribution of these indices are demonstrated and analyzed, and results show how the derived measures reflect changes in spatial data arrangement