data clustering with leaders and subleaders algorithm
... subgroups/subclusters as required in certain applications. In optical character recognition data set, there are 10 classes corresponding to the digits 0–9. There would be several subgroups/subclusters in each class depending on the attribute values of the features. For example, digits may be printed ...
... subgroups/subclusters as required in certain applications. In optical character recognition data set, there are 10 classes corresponding to the digits 0–9. There would be several subgroups/subclusters in each class depending on the attribute values of the features. For example, digits may be printed ...
Automated linking PUBMED documents with GO terms using SVM
... corpus collected from the MEDLINE database and 27% of the GO terms were found in the current edition of the Unified Medical Language System (UMLS). A recent research work of Raychaudri et al. employs a “maximum entropy” technique to categorize 21 GO terms using training and test documents extracted ...
... corpus collected from the MEDLINE database and 27% of the GO terms were found in the current edition of the Unified Medical Language System (UMLS). A recent research work of Raychaudri et al. employs a “maximum entropy” technique to categorize 21 GO terms using training and test documents extracted ...
4 Genetic Programming in Data Mining
... data and turn it to the useful information. For example, in evaluating loan applications, by improving ability to predict bad loans, the company can substantially reduce its loan losses. In medical area, we could predict the probability of presence of the heart disease in a new patient by learning f ...
... data and turn it to the useful information. For example, in evaluating loan applications, by improving ability to predict bad loans, the company can substantially reduce its loan losses. In medical area, we could predict the probability of presence of the heart disease in a new patient by learning f ...
Semi-Lazy Learning: Combining Clustering and Classifiers to Build
... overall provides better results for a variety of data sets. For only one data set was the BASE ACCURACY significantly greater than the SEGMENTED ACCURACY. When using decision trees the BREAST CANCER, IRIS and HYPOTHYROID data sets benefited from segmentation whilst the VOTE data set did not benefit ...
... overall provides better results for a variety of data sets. For only one data set was the BASE ACCURACY significantly greater than the SEGMENTED ACCURACY. When using decision trees the BREAST CANCER, IRIS and HYPOTHYROID data sets benefited from segmentation whilst the VOTE data set did not benefit ...
Classification Using Decision Trees
... 1. Some DT can only deal with binary valued target classes, others are able to assign records to an arbitrary number of classes, but are error prone when the number of training examples per class gets small. This can happen rather quickly in a tree with many levels and many branches per node. 2. The ...
... 1. Some DT can only deal with binary valued target classes, others are able to assign records to an arbitrary number of classes, but are error prone when the number of training examples per class gets small. This can happen rather quickly in a tree with many levels and many branches per node. 2. The ...
Data Mining Techniques to Find Out Heart Diseases: An
... E. Data Mining Through Genetic Algorithms We start out with a randomly selected first generation. Every string in this generation is evaluated according to its quality, and a fitness value is assigned. Next, a new generation is produced by applying the reproduction operator. Pairs of strings of the ...
... E. Data Mining Through Genetic Algorithms We start out with a randomly selected first generation. Every string in this generation is evaluated according to its quality, and a fitness value is assigned. Next, a new generation is produced by applying the reproduction operator. Pairs of strings of the ...
Initialization of Big Data Clustering
... especially for the smaller values of k. A strong variation of the SSE difference for the dataset S1 is most likely a consequence of higher probability to get stuck in a local minimum. For USPS, the SSE difference is close to zero for all the values of k, which indicates that the accuracy of Algorithm ...
... especially for the smaller values of k. A strong variation of the SSE difference for the dataset S1 is most likely a consequence of higher probability to get stuck in a local minimum. For USPS, the SSE difference is close to zero for all the values of k, which indicates that the accuracy of Algorithm ...
Classification of Titanic Passenger Data and Chances of
... modern data mining tools (Weka) and an available dataset we take a look at what factors or classifications of passengers have a persuasive relationship towards survival for passengers that took that fateful trip on April 10, 1912. The analysis looks to identify characteristics of passengers - cabin ...
... modern data mining tools (Weka) and an available dataset we take a look at what factors or classifications of passengers have a persuasive relationship towards survival for passengers that took that fateful trip on April 10, 1912. The analysis looks to identify characteristics of passengers - cabin ...
PAKDD`15 presentation
... If there were more B products from A00002 category, only the first one was taken into account The number of sessions in which the B product in a given time slot appeared had to be greater than one Sessions without A00002 category got missing value for feature ...
... If there were more B products from A00002 category, only the first one was taken into account The number of sessions in which the B product in a given time slot appeared had to be greater than one Sessions without A00002 category got missing value for feature ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.