Approximate kNN (k-nearest neighbor) techniques using binary hash functions are among the most commonly used approaches for overcoming the prohibitive cost of performing exact kNN queries. However, the success of these techniques largely depends on their hash functions’ ability to distinguish kNN items; that is, the kNN items retrieved based on data items’ hashcodes, should include as many true...

In this project we present a heuristic learning process by training ANN (artificial neural network) and KNN (k-nearest neighbor) using the best number of steps, gained from A*, from randomly generated states to the goal. After training ANN and KNN, the mixture of Experts is discussed and the empirical data are collected to demonstrate the feasibility and accuracy of combination of ANN and KNN i...

Interpretability, usability, and boundary points detection of the clustering results are of fundamental importance. The cluster boundary detection is important for many real world applications, such as geo-spatial data analysis and point-based computer graphics. In this paper, we have proposed an efficient solution for finding boundary points in multi-dimensional datasets that uses the BORDER [...

Four versions of a k-nearest neighbor algorithm with locally adaptive k are introduced and compared to the basic k-nearest neighbor algorithm (kNN). Locally adaptive kNN algorithms choose the value of k that should be used to classify a query by consulting the results of cross-validation computations in the local neighborhood of the query. Local kNN methods are shown to perform similar to kNN i...

The use of satellite data in forest inventories makes it possible to do a wall-to-wall mapping of forest variables. This type of raster databases with estimated forest variables provides a great potential for planning how the forest resources should be utilized, and can be used as input ecological models. There are different remote sensing methods that can be used to map forest resources. One i...

The random assignment problem asks for the minimum-cost perfect matching in the complete n × n bipartite graph Knn with i.i.d. edge weights, say uniform on [0, 1]. In a remarkable work by Aldous (2001), the optimal cost was shown to converge to ζ(2) as n → ∞, as conjectured by Mézard and Parisi (1987) through the so-called cavity method. The latter also suggested a non-rigorous decentralized st...

In this paper, an approach to biometric verification based on human body communication (HBC) is presented for wearable devices. For this purpose, the transmission gain S21 of volunteer's forearm is measured by vector network analyzer (VNA). Specifically, in order to determine the chosen frequency for biometric verification, 1800 groups of data are acquired from 10 volunteers in the frequency ra...

Microarrays have unique ability to probe thousands of genes at a time that makes it a useful tool for variety of applications, ranging from diagnosis to drug discovery. However, data generated by microarrays often contains multiple missing gene expressions that affect the subsequent analysis, as most of the times these missing values are ignored. In this paper we have analyzed how accurate esti...

A successful Case-Based Reasoning (CBR) system highly depends on how to design an accurate and efficient case retrieval mechanism. In this research we propose a Weighted Feature C-means clustering algorithm (WF-Cmeans) to group all prior cases in the case base into several clusters. In WF-Cmeans, the weight of each feature is automatically adjusted based on the importance of the feature to clus...

H. Altay Güvenir and Aynur Akkuş Department of Computer Engineering and Information Science Bilkent University, 06533, Ankara, Turkey fguvenir, [email protected] Abstract. This paper proposes an extension to the k Nearest Neighbor algorithm on Feature Projections, called kNNFP. The kNNFP algorithm has been shown to achieve comparable accuracy with the well-known kNN algorithm. However, k...