An Incremental Hierarchical Data Clustering Algorithm Based on
... design of modern clustering algorithms is that, in many applications, new data sets are continuously added into an already huge database. As a result, it is impractical to carry out data clustering from scratch whenever there are new data instances added into the database. One way to tackle this cha ...
... design of modern clustering algorithms is that, in many applications, new data sets are continuously added into an already huge database. As a result, it is impractical to carry out data clustering from scratch whenever there are new data instances added into the database. One way to tackle this cha ...
A MapReduce Algorithm for Polygon Retrieval
... independent map and reduce tasks over several nodes of a large data center and process them in parallel. MapReduce can effectively leverage data locality and processing on or near the storage nodes and result in faster execution of the jobs. The framework consists of one master node and a set of sla ...
... independent map and reduce tasks over several nodes of a large data center and process them in parallel. MapReduce can effectively leverage data locality and processing on or near the storage nodes and result in faster execution of the jobs. The framework consists of one master node and a set of sla ...
Graph-Based Structures for the Market Baskets Analysis
... given by the conditional probability of B given A, P(B|A), which is equal to P({A,B})/P(A). The Apriori algorithm was implemented in commercial packages, such as Enterprise Miner from the SAS Institute [SAS 2000]. As input, this algorithm uses a table with purchase transactions. Each transaction con ...
... given by the conditional probability of B given A, P(B|A), which is equal to P({A,B})/P(A). The Apriori algorithm was implemented in commercial packages, such as Enterprise Miner from the SAS Institute [SAS 2000]. As input, this algorithm uses a table with purchase transactions. Each transaction con ...
THE SMALLEST SET OF CONSTRAINTS THAT EXPLAINS THE
... found a significant property of the data. However, this does not necessarily fully explain the data. For example, suppose we are performing k-means clustering. A natural choice would be to use the k-means cost function as the test statistic. Nonrandom data is expected to have some structure, resulti ...
... found a significant property of the data. However, this does not necessarily fully explain the data. For example, suppose we are performing k-means clustering. A natural choice would be to use the k-means cost function as the test statistic. Nonrandom data is expected to have some structure, resulti ...
Computational Intelligence in Data Mining
... be appropriate and matching a particular algorithm with the overall criteria of the KDD process (e.g. the end-user may be more interested in understanding the model than its predictive capabilities.) One can identify three primary components in any data mining algorithm: model representation, model ...
... be appropriate and matching a particular algorithm with the overall criteria of the KDD process (e.g. the end-user may be more interested in understanding the model than its predictive capabilities.) One can identify three primary components in any data mining algorithm: model representation, model ...
Randomized local-spin mutual exclusion
... Increment promotion token whenever releasing a node Perform deterministic promotion according to promotion index in addition to randomized promotion ...
... Increment promotion token whenever releasing a node Perform deterministic promotion according to promotion index in addition to randomized promotion ...
A Distribution-Based Clustering Algorithm for Mining in Large
... 3.2 The Statistic Model for our Cluster Definition In the following, we analyze the probability distribution of the nearest neighbor distances of a cluster. This analysis is based on the assumption that the points inside of a cluster are uniformly distributed, i.e. the points of a cluster are distri ...
... 3.2 The Statistic Model for our Cluster Definition In the following, we analyze the probability distribution of the nearest neighbor distances of a cluster. This analysis is based on the assumption that the points inside of a cluster are uniformly distributed, i.e. the points of a cluster are distri ...
Correlation based Effective Periodic Pattern Extraction from
... noise. This STNR algorithm uses a Suffix tree data structure [11], [12], [13] that has been proven to be very useful in string processing. It can be efficiently used to find a substring in the original string and to find the frequent substrings. But this STNR algorithm is not the most appropriate fo ...
... noise. This STNR algorithm uses a Suffix tree data structure [11], [12], [13] that has been proven to be very useful in string processing. It can be efficiently used to find a substring in the original string and to find the frequent substrings. But this STNR algorithm is not the most appropriate fo ...
Textual data mining for industrial knowledge management and text
... Textual databases are useful sources of information and knowledge and if these are well utilised then issues related to future project management and product or service quality improvement may be resolved. A large part of corporate information, approximately 80%, is available in textual data formats ...
... Textual databases are useful sources of information and knowledge and if these are well utilised then issues related to future project management and product or service quality improvement may be resolved. A large part of corporate information, approximately 80%, is available in textual data formats ...
no - CENG464
... – CART: finds multivariate splits based on a linear comb. of attrs. • Which attribute selection measure is the best? – Most give good results, none is significantly superior than others ...
... – CART: finds multivariate splits based on a linear comb. of attrs. • Which attribute selection measure is the best? – Most give good results, none is significantly superior than others ...
Online Publishing @ www.publishingindia.com DISTRIBUTED
... amount. The time complexity of our algorithm as obtained is O(n2) whereas the time complexity suggested by Xindong is higher than O(n2), moreover the space complexity is also optimized as we have removed the normalization step where weight of rule and frequency has been multiplied in the Xindong met ...
... amount. The time complexity of our algorithm as obtained is O(n2) whereas the time complexity suggested by Xindong is higher than O(n2), moreover the space complexity is also optimized as we have removed the normalization step where weight of rule and frequency has been multiplied in the Xindong met ...
Data Mining
... If the CV estimates are from different datasets, they are no longer paired (or maybe we have k estimates for one scheme, and j estimates for the other one) ● Then we have to use an unpaired ttest with min(k , j) – 1 degrees of freedom ● The estimate of the variance of the difference of the mea ...
... If the CV estimates are from different datasets, they are no longer paired (or maybe we have k estimates for one scheme, and j estimates for the other one) ● Then we have to use an unpaired ttest with min(k , j) – 1 degrees of freedom ● The estimate of the variance of the difference of the mea ...
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.