KNN-Joins Using a Hybrid Approach

Aim: Coronary illness is one of the main sources of mortality in this current world. A basic test in the field of clinical information assessment is the expectation of cardiovascular infection. The fundamental target of this paper is to work on the exactness of the Data mining algorithms being used to improve heart disease prediction. We will be comparing the novel Random forest with K Nearest Neighbor to find out which of these can give us the best accuracy. Material and Methods: The study used 98 samples with Novel Random Forest and K Nearest Neighbor is executed with varying training and testing splits for predicting the accuracy for heart disease prediction with the 80% of G-power value and data about cardiovascular disease was gathered from numerous web sources, including latest research findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the accuracy rate of the classifiers is examined using the heart disease dataset.. There was a statistical insignificant difference among Innovative Random Forest and K Nearest Neighbor p = 0.484 (p>0.05). Results and Discussion: The accuracy of predicting heart disease in Novel Random Forest 90.16% and K Nearest Neighbor 67.21% is obtained. Conclusion: Prediction of Heart disease using the innovative Random Forest (RF) technique appears to be a significant improvement over the K Nearest Neighbor (KNN) algorithm in terms of accuracy.

Aim: Prediction of coronary illness utilizing novel Novel k nearest neighbor (KNN) and contrasting its accuracy with decision tree algorithm. Materials and Methods: Two gatherings are proposed for foreseeing the accuracy (%) of coronary illness. To be specific, novel Novel k nearest neighbor and decision tree algorithm. Here we take 20 examples each for assessment and look at. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result: The decision tree gives better accuracy (84.95%) contrasted with the Novel k nearest neighbor accuracy (76.29 %). Along these lines the factual meaning of the decision tree is superior to the novel k nearest neighbor algorithm with significance value of 0.115. Conclusion: From the outcome, it may very well be inferred that the decision tree helps in anticipating the coronary illness with more precision contrasted with the novel k nearest neighbor algorithm.

FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors

<p>Satellite based rainfall estimates (SBRE) are used as an alternative to gauge rainfall in hydrological studies particularly for basins with data issues. However, these data products exhibit errors which cannot be always corrected by bias correction methods such as Ratio Bias Correction (RBC). Data fusion or data merging can be a potentially good approach in merging various SBREs to obtain a fused dataset, which can benefit from all the data sources and may minimise the error in rainfall estimates. Data merging methods which are commonly applied in meteorology and hydrology are: Arithmetic merging method (AMM), Inverse error squared weighting (IESW) and Error variance (EV). Among these methods EV is popular, which merges bias corrected SBREs using the minimisation of variance principle.</p><p>In this research we propose using K Nearest Neighbour (KNN) as a data merging method. KNN has a particular advantage as it does not depend upon any specific statistical model to merge data and presents a great flexibility as the value of K (the number of neighbours to be chosen) can be varied to suit the purpose (for example, choosing different K values for different seasons). In this research it is proposed to compute the distances of bias corrected SBREs of the training data from the gauge data and to assign the SBRE with the minimum distance as the class C where C = 1, 2, 3,…, number of SBREs. In validation each data point consisting of a value of each SBRE may be compared with the data points from the training set and the class of the data point(s) closest to this data point is assigned as the class of the validation data point.</p><p>The KNN approach as a data merging method was applied to the Indus basin in Pakistan. Three satellite rainfall products CMORPH, PERSIANN CDR and TRMM 3B42 with 0.25° x 0.25° spatial and daily temporal resolution were used. Based on the climatic and physiographic features the Indus basin was divided into four zones. Rainfall products were compared at daily, weekly, fortnightly, monthly and seasonally whereas spatial scales were gauge location, zonal scales and basin scale. The RBC method was used to correct the bias. The KNN method with K=1, 3 and 5 was used and compared with other merging methods namely AMM, IESW and EV. The results were compared in two seasons i.e. non-wet and wet season. AMM and EV methods performed similarly whereas IESW performed poorly at zonal scales. KNN merging method outperformed all other merging methods and gave lowest error across the basin. The daily normalised root mean square error at the Indus basin scale was reduced to 0.3, 0.45 and 0.45 respectively with KNN, AMM and EV whereas this error was 0.8, 0.65 and 0.53 respectively in CMORPH, PERSIANN CDR and TRMM datasets. The KNN merged product gave lowest error at daily scale in calibration and validation period which justifies that merging with KNN improves rainfall estimates in sparsely gauged basins.</p><p> </p><p><strong>Key words:</strong> Merging, data fusion, K nearest neighbour, KNN, error variance, Indus.</p>

The spatial distribution of bright and faint galaxies in the Virgo supercluster is compared using the nearest neighbour test, cluster and percolation analysis. There are no differences in the spatial distribution of bright and faint galaxies down to absolute magnitude -15.0 (H = 100 km s−1 Mpc−1) except for most giant galaxies, which are located in the high density regions and are absent in the lowest density regions. Bright and faint galaxies are situated in statistically identical systems, and the ratio of faint to bright galaxies is nearly the same in high and low density regions. There are no isolated galaxies in the region under study.

Traditional k-nearest neighbor methods couldn’t be able to correctly classify objects when their k nearest neighbors are dominated by other classes. This paper formulates a two-class classification problem, and applies a modified k-nearest neighbors (KNN) classifier algorithm based on maximal coherence, validity ratings, and k-fold cross validation to classify the test samples. We build a validity score for the pairs of sample and their surroundings according to their labels. The k nearest neighbors (including the unknown test object) of each sample in the training set as well as the unknown test object itself will be determined. The unknown test object will be tentatively assigned to a class membership. Then we use the validity scores to quantify the degree to which a pre-determined group of samples resemble their k nearest neighbors. A classifier is designed which take into account the coherence and validity ratings. A numerical example demonstrates the effectiveness of the algorithm in detail. The enhanced KNN method is compared with the conventional KNN and the modified KNN method on both real world wine data and photo-thermal infrared imaging spectroscopy (PT-IRIS) data for up to 20 different k values. Classification accuracy of KNN method and our method in terms of various combinations of k-value and k-fold cross validation are compared. The experimental results show that the proposed enhanced KNN method outperforms the conventional KNN and the modified KNN method on both real world wine data and PT-IRIS data. In addition, the classification accuracy of both the conventional KNN and our method increase drastically when k = 5. The average classification accuracy of our method on the PT-IRIS data featuring small sample size and high overlap is 97.87%.

K-nearest neighbor (kNN) search has wide applications in many areas, including data mining, machine learning, statistics and many applied domains. Inspired by the success of ensemble methods and the flexibility of tree-based methodology, we propose random projection forests (rpForests), for kNN search. rpForests finds kNNs by aggregating results from an ensemble of random projection trees with each constructed recursively through a series of carefully chosen random projections. rpForests achieves a remarkable accuracy in terms of fast decay in the missing rate of kNNs and that of discrepancy in the kNN distances. rpForests has a very low computational complexity. The ensemble nature of rpForests makes it easily run in parallel on multicore or clustered computers; the running time is expected to be nearly inversely proportional to the number of cores or machines. We give theoretical insights by showing the exponential decay of the probability that neighboring points would be separated by ensemble random projection trees when the ensemble size increases. Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.

As Geographic Information Systems (GIS) technologies have evolved, more and more GIS applications and geospatial data are available on the web. Spatial objects in a given query range can be retrieved using spatial range query ? one of the most widely used query types in GIS and spatial databases. However, it can be challenging to retrieve these data from various web applications where access to the data is only possible through restrictive web interfaces that support certain types of queries. A typical scenario is the existence of numerous business web sites that provide their branch locations through a limited "nearest location" web interface. For example, a chain restaurant's web site such as McDonalds can be queried to find some of the closest locations of its branches to the user's home address. However, even though the site has the location data of all restaurants in, for example, the state of California, it is difficult to retrieve the entire data set efficiently due to its restrictive web interface. Considering that k-Nearest Neighbor (k-NN) search is one of the most popular web interfaces in accessing spatial data on the web, this paper investigates the problem of retrieving geospatial data from the web for a given spatial range query using only k-NN searches. Based on the classification of k-NN interfaces on the web, we propose a set of range query algorithms to completely cover the rectangular shape of the query range (completeness) while minimizing the number of k-NN searches as possible (efficiency). We evaluated the efficiency of the proposed algorithms through statistical analysis and empirical experiments using both synthetic and real data sets.

K-nearest neighbor (kNN) search is a fundamental operation in various point cloud applications, such as autonomous driving. However, the heavy computational intensity and memory demands of kNN search pose significant challenges for efficient implementation, especially in resource-constrained scenarios. To address these challenges, we propose PICK, a processing-in-memory (PIM) architecture designed to accelerate kNN search in point cloud applications. PICK leverages bit-serial-based PIM (BS-PIM) and customized circuits to efficiently handle key operations of kNN search: distance calculation and top-k selection. The run-time off-chip access is eliminated thanks to the large on-chip memory. For distance calculation, we introduce a bit-width clipping technique to reduce the latency of bit-serial execution with negligible accuracy degradation, providing flexible trade-offs between performance and precision. Besides, we propose a filtering-and-selection strategy that realizes approximately constant time complexity for arbitrary values of k. Furthermore, a two-stage pipeline is implemented to parallelize distance calculation and top-k search, effectively hiding latency and improving throughput. According to our experiments, PICK achieves $4.17 \times$ speedup and a $4.42 \times$ energy saving over the state-of-the-art design.

We introduce a new 2-D hexagon technique to probe the topological structure of the universe, in which we map regions of the sky with high and low galaxy densities onto a 2-D lattice of hexagon unit cells, We define filled cells as corresponding to high density regions and empty cells as corresponding to low density region. The number of filled cells and empty cells are kept same by controlling the size of the cells. By analyzing the six neighbors of each hexagon we can get and compare statistical topological properties of the high density and low density regions in the universe, in order to have a better understanding of the evolution of the universe. We apply this hexagon method on 2MASS data and discover significant topological differences between the high density and low density regions. Both regions have significant (>5 Sigma) topological shifts from the binomial distribution or the random distribution.

For the problem that the prediction accuracy of real-valued attribute data is not high, a modeling method named PR-KNN (Polynomial Regression and K Nearest Neighbor) is proposed, which is based on combination of KNN (K Nearest Neighbor) algorithm and Polynomial Regression model. Firstly, K nearest decision attribute values in training samples are selected by using KNN algorithm. Secondly, these K nearest decision attribute values are modeled by using Polynomial Regression method. And this method is applied to aftershock prediction. Experimental data are the sequence data of aftershocks with magnitude greater than or equal to 4.0 from Wenchuan earthquake. Comparing with traditional KNN regression algorithm and Distance-Weighted KNN regression algorithm, experimental results show that the maximum relative error predicted by PR-KNN reduces by 6.012% and 7.751% respectively, and maximum absolute error reduces by 0.367 and 0.473 respectively.

A very important part of machine learning is classification. One of the most important classification algorithms is the k-nearest neighbour algorithm. This algorithm works on the principle that a point belongs to the same class as the majority of its closest neighbours. In this approach, a point qualifies as one of the closest neighbours of another point based on the parameter ‘k’. We determine whether a point has a bearing on another neighbour’s class if it is one of the first ‘k’-nearest neighbours of the point in question. A major disadvantage of this algorithm is the fact that we are bound to take exactly ‘k’-nearest neighbours irrespective of the distance between a point and the neighbour. This is a major anomaly. We decide that we shall determine the importance of a point in an algorithm based on proximity to another point but do not pay any heed to the distance between those two points. Many solutions have been proposed to reduce the effect of this problem in the algorithm. This paper gets rid of this problem altogether and adds another dimension to the nearest neighbour classification model.

Location estimation as one of the most popular research areas has been recently attended because of wide range of its applications. K Nearest Neighbor (KNN) is a basic deterministic algorithm for locating which is widely used in fingerprinting approach. The performance of the KNN can be improved extensively by employing appropriate selection algorithm. In this paper, a novel algorithm called Dynamic KNN (D-KNN) which uses Angle of Arrival (AOA) and KNN as a hybrid method is proposed. This method comparing with KNN algorithm with constant K, selects the best number of nearest neighbors dynamically. It utilizes the adaptive antenna system to determine the user locative area by intersection of several obtained AOA. The best K neighbors which are located in the determined area can be selected to employ in the KNN. Analysis and simulation results are reported the best overall performance of D-KNN in different conditions.

The k Nearest Neighbors (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space. Efforts have been made to map data to a better feature space either implicitly with kernel functions, or explicitly through learning linear or nonlinear transformations. However, all these methods use pre-determined distance or similarity functions, which may limit their learning capacity. In this paper, we propose a novel deep learning architecture, which is called the Deep Similarity-Enhanced K Nearest Neighbors (DSE-KNN), to learn an optimized similarity function of the data directly towards the goal of optimizing the KNN decision making. In other words, the type of similarity function that is used in our method is not pre-determined but rather learned to map data to a high-dimensional feature space where the accuracy of the KNN decision making is maximized. Experimental results show that DSE-KNN outperforms other common machine learning methods on classifying different types of disease datasets and predicting daily price direction of different stock ETFs.

Sales data analysis is an important aspect of modern businesses, as it enables them to gain insights into customer preferences and behavior, optimize marketing strategies, and improve revenue. With the rise of big data and machine learning, businesses can now leverage these tools to analyze sales data and gain even deeper insights. This paperfocuses on analyzing sales data using machine learning algorithms, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm. KNN is a popular machine learning algorithm used in classification and regression problems, and it works by identifying the K closest data points to a new observation and predicting its class or value based on the majority class or average value of its nearest neighbors. The dataset used in this paperis from a retail store and includes information on product type, sales channel, customer demographics, and sales volume over a period of several years. The paper will use Python and various machine learning libraries to perform data preprocessing, exploratory data analysis, feature engineering, and modeling. Specifically, KNN will be used to develop a predictive model for sales forecasting, which can help businesses optimize their inventory and production planning. The paper will also use clustering techniques, such as K-Means, to identify customer segments based on their purchasing behavior and demographics, and association rule mining to identify product affinities and recommend complementary products. The results of this analysis can help businesses gain a deeper understanding of their sales data and make data-driven decisions to improve their bottom line. By leveraging the power of KNN and other machine learning algorithms, businesses can gain insights into customer behavior and preferences, optimize their marketing strategies, and improve their overall revenue. Keywords: KNN Algorithm, Data Analytics, Machine Learning, Support Vector Machine.