eDAR Algorithm for Continuous KNN Queries Based on Pine

2011 ◽  
pp. 154-174
Author(s):  
Maytham Safar ◽  
Dariush Ebrahimi
Keyword(s):  
Shortest Path ◽  
Nearest Neighbor ◽  
Query Point ◽  
Spatial Network ◽  
K Nearest Neighbor ◽  
Network Expansion ◽  
Shortest Distance ◽  
Stationary Objects ◽  
Split Point ◽  
Euclidean Distances

The continuous K nearest neighbor (CKNN) query is an important type of query that finds continuously the KNN to a query point on a given path. We focus on moving queries issued on stationary objects in Spatial Network Database (SNDB) The result of this type of query is a set of intervals (defined by split points) and their corresponding KNNs. This means that the KNN of an object traveling on one interval of the path remains the same all through that interval, until it reaches a split point where its KNNs change. Existing methods for CKNN are based on Euclidean distances. In this paper we propose a new algorithm for answering CKNN in SNDB where the important measure for the shortest path is network distances rather than Euclidean distances. We propose DAR and eDAR algorithms to address CKNN queries based on the progressive incremental network expansion (PINE) technique. Our experiments show that the eDAR approach has better response time, and requires fewer shortest distance computations and KNN queries than approaches that are based on VN3 using IE.

scholarly journals Klasifikasi Kematangan Jeruk Lemon Menggunakan Metode K-Nearest Neighboard

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Indrawati Indrawati
Keyword(s):  
Moisture Content ◽  
Water Content ◽  
Nearest Neighbor ◽  
Training Image ◽  
K Nearest Neighbor ◽  
Oil Glands ◽  
Shortest Distance ◽  
Approximate Distance ◽  
Dark Green

Abstrak— Klasifikasi jeruk lemon adalah disiplin bidang ilmu yang menggambarkan identifikasi jeruk berdasarkan sifatnya. Beberapa sifat dari jeruk lemon, diantaranya kulit terluar lemon kaya akan kelenjar minyak, kematangan ditandai dengan warna kulit kuning terang. Jeruk lemon yang berwarna hijau gelap, menandakan jeruk lemon tersebut belum matang dan kandungan air di dalamnya akan lebih sedikit. Pada penelitian ini kematangan diklasifikasikan menggunakan metode K-Nearest Neighbor. Hasilnya adalah klasifikasi kematangan dengan kadar air 90% jarak terdekat rata-rata sebesar 10,86 dengan akurasi 85%, sedangkan pada pengujian jeruk lemon dengan kematangan 80% diperoleh jarak terdekat 7,3 dengan akurasi 81%. Pada pengujian dengan kematngan dengan kadar air 70 persen diperoleh jarak rata-rata terdekat 19,4 dan akurasi 86,11%. Untuk jeruk lemon dengan kategori tidak matang dengan kadar air 50% diperoleh jarak terdekat sebesar 19,46 dan akurasi 88,9 % , sedangkan pada pengujian jeruk lemon mentah dengan kadar air 40% diperoleh jarak terdekat 16,19 dan akurasi 88,73 dan untuk pengujian jeruk lemon tidak matang dengan kadar air 30% diperoleh klasifikasi dengan jarak terdekat rata-rata sebesar 1,85 dan akuras 84,13%. Hal ini menunjukkan bahwa sistem klasifikasi dengan menggunakan metode K-NN cukup baik, indikatornya adalah jarak terdekat rata-rata yang dihasilkan antara citra uji dan citra training bernilai antara 1,85 sampai 19,46 dan akurasinya antara 81% sampai88,89 %.Kata kunci— Akurasi, Jeruk lemon, Klasifikasi, kedekatan, tetangga, uji.Abstract— Classification of lemon is the discipline of science that describes the identification of citrus by its character. Some characterof lemon, lemon outer shell is rich in oil glands, maturity is marked by bright yellowskin color, lemon which is dark green, indicates the immature lemon and water content in it will be less. In this study maturity are classified using K-Nearest Neighbor method. The result is a classification of maturity with 90% moisture content has shortest distance average of 10.86 with an accuracy of 85%, while in the testing of lemon with a maturity of 80% obtained the nearest distance of 7.3 with an accuracy of 81%. In maturity testing with a water content of 70 percent derived average approximate distance of 19.4 and 86.11% accuracy. For the lemon with the category of immature by moisture content of 50% obtained the nearest distance at 19.46 and accuracy of 88.9%, while in the testing of raw lemon with a moisture content of 40% obtained the nearest distance 16.19 and accuracy of 88.73 and for testing of immature lemon with a water content of 30% obtained classifications with the average nearest distance of 1.85 and accuracy of 84.13%. This indicates that the classification system using K-NN was very good, the indicator is the average nearest distance between the tested images and training image between 1.85 to 19.46 valuable and accuracy between 81% to 88.89%.Keywords— Accuracy, Lemon, classification,nearets, neighbors, test.

scholarly journals An Instance Selection Algorithm Based On Reverse k Nearest Neighbor

2013 ◽  
Vol 10 (7) ◽  
pp. 1858-1861
Author(s):  
Y. Jagruthi ◽  
Dr. Y. Ramadevi ◽  
A. Sangeeta
Keyword(s):  
Data Reduction ◽  
Nearest Neighbor ◽  
Reduction Rate ◽  
Measure Data ◽  
Instance Selection ◽  
Query Point ◽  
Selection Algorithm ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Reverse Nearest Neighbor

Classification is one of the most important data mining techniques. It belongs to supervised learning. The objective of classification is to assign class label to unlabelled data. As data is growing rapidly, handling it has become a major concern. So preprocessing should be done before classification and hence data reduction is essential. Data reduction is to extract a subset of features from a set of features of a data set. Data reduction helps in decreasing the storage requirement and increases the efficiency of classification. A way to measure data reduction is reduction rate. The main thing here is choosing representative samples to the final data set. There are many instance selection algorithms which are based on nearest neighbor decision rule (NN). These algorithms select samples on incremental strategy or decremental strategy. Both the incremental algorithms and decremental algorithms take much processing time as they iteratively scan the dataset. There is another instance selection algorithm, reverse nearest neighbor reduction (RNNR) based on the concept of reverse nearest neighbor (RNN). RNNR does not iteratively scan the data set. In this paper, we extend the RNN to RkNN and we use the concept of RNNR to RkNN. RkNN finds the query objects that has the query point as their k nearest-neighbors. Our approach utilizes the advantage of RNN and proposes to use the concept of RkNN. We have taken the dataset of theatres, hospitals and restaurants and extracted the sample set. Classification has been done the resultant sample data set. We observe two parameters here they are classification accuracy and reduction rate.

INCREMENTAL K-NEAREST-NEIGHBOR SEARCH ON ROAD NETWORKS

2008 ◽  
Vol 09 (04) ◽  
pp. 455-470 ◽  
Author(s):  
GENG ZHAO ◽  
KEFENG XUAN ◽  
DAVID TANIAR ◽  
BALA SRINIVASAN
Keyword(s):  
Road Network ◽  
Nearest Neighbor ◽  
Road Networks ◽  
Nearest Neighbor Search ◽  
Query Point ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Neighbor Search ◽  
New Type ◽  
K Nearest Neighbor Search

Most query search on road networks is either to find objects within a certain range (range search) or to find K nearest neighbors (KNN) on the actual road network map. In this paper, we propose a novel query, that is, incremental k nearest neighbor (iKNN). iKNN can be defined as given a set of candidate interest objects, a query point and the number of objects k, find a path which starts at the query point, goes through k interest objects and the distance of this path is the shortest among all possible paths. This is a new type of query, which can be used when we want to visit k interest objects one by one from the query point. This approach is based on expanding the network from the query point, keeping the results in a query set and updating the query set when reaching network intersection or interest objects. The basic theory of this approach is Dijkstra's algorithm and the Incremental Network Expansion (INE) algorithm. Our experiment verified the applicability of the proposed approach to solve the queries, which involve finding incremental k nearest neighbor.

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