[PDF] Top 20 A cultural algorithm for data clustering
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A cultural algorithm for data clustering
... following clustering algorithms: K-means, GA, TS, SA, ACO and PSO which are taken from Niknam and Amiri (2010) ...between data vectors within a cluster and the centroid of this cluster, as defined in ... See full document
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uCLUST A new algorithm for clustering unstructured data
... Unstructured data files often include text and multimedia ...the data in any organization is ...unstructured data in enterprises is growing significantly often many times faster than structured ... See full document
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Clustering Algorithm for Temporal Data Mining: An Overview
... related data such stock market data or logged ...new data coming in, which sometimes causes the need for a challenging real time ...analysis. Data mining in such databases commonly includes ... See full document
5
Weather Prediction Using J48, EM And K-Means Clustering Algorithms
... ABSRACT: Data mining is the computer assisted process of digging through and analyzing immense sets of data and then extracting the relevant ...data. Data mining tools predicts behaviors and ... See full document
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High Dimensional Data used in Consensus Neighbour Clustering with Fuzzy Based K-Means and Kernel Mapping
... local data centers is not only a feasible option, but also frequently leads to improvement over the centroid-based ...neighbouring clustering in high dimensional data algorithm for the ... See full document
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Evaluation of BIRCH Clustering Algorithm for Big Data
... k-mean clustering algorithm by parallelization ...the data points are divided equally based upon the processors and then the calculations are done on the divided ... See full document
5
A New Clustering Algorithm On Nominal Data Sets
... given data set, we introduce a new variable Distance, the calculation of which is shown in ...ex-Olary algorithm tries to get the global minimum of the parameter ...Olary algorithm N times, each time ... See full document
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Mixture Model Clustering Using Variable Data Segmentation and Model Selection: A Case Study of Genetic Algorithm
... genetic algorithm for mixture model clustering using variable data segmentation and model selection is proposed in this ...model clustering of Ruspini data ...the data set were ... See full document
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Development of a Data Clustering Algorithm for Predicting Heart
... of data set into subsets so that the data in each of the subset share a general feature, frequently the proximity with regard to some defined distance measure [36], is known as ...The clustering ... See full document
6
Clustering Algorithm for Spatial Data Mining: An Overview
... or data point is assigned to the nearest cluster (the cluster which it is most similar to) using a measure of distance or similarity like the Euclidean Distance Measure or Manhattan/City-Block Distance ...K-Means ... See full document
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A Scalable Approach for Data-Driven Taxi Ride Sharing
... the data is too large to be stored in main memory and must be accessed sequentially, such as from a disk, and where we must use as little memory as ...Our algorithm is based on recent theoretical results, ... See full document
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HIERARCHICAL FUZZY RELATIONAL CLUSTERING ALGORITHM FOR SENTENCE LEVEL TEXT
... e Clustering is an effective technique for data analysis and has various applications in a wide variety of ...of clustering categorized into hard clustering and soft ...clustering. ... See full document
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External Support Vector Machine Clustering
... binary clustering runs on the 592/595/597 DNA hairpin data are demonstrated in figure ...595 data sets form a separate cluster from the 597 data set with almost 90% accuracy (an accuracy of ... See full document
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Study on swarm optimization clustering algorithm
... each data sample can always be incorporated into a ...the algorithm is that it cannot show clearly the relation between data samples and clustering center, and in practice it is hard to find a ... See full document
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Classification and Analysis of High Dimensional Datasets using Clustering and Decision tree
... of data grouping (clustering) is simple to use and in its nature and is very near to the human way of thinking; whenever they are offered with a large amount of data, humans are habitually tend to ... See full document
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Semi Supervised Clustering Ensemble Approaches Over Multiple Datasets
... learning algorithm is ordering data of comparable wide research, likewise in application, the objective space with dynamic learning algorithm, to streamline the point name ...neighbour data ... See full document
5
Transmogrified Imputation Algorithm for Clustering Data in Missing Data Imputation
... Naïve Bayes technique is one of the most useful machine learning techniques based on computing probabilities. It analyses relationship between each independent variable and the dependent variable to derive a conditional ... See full document
5
A Multi Agent Bio Inspired System to Map Learners with Learning Resources using Clustering Based Personalization
... engine. Clustering is defined as a technique found in data mining for identifying interesting patterns in the ...similar data with the K-Means ...K-means clustering approach optimized with FA ... See full document
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EVOLUTIONARY CLUSTERING ALGORITHM FOR DISCRETE DATA
... based clustering algorithm has several useful features: (i) it uses an entropy based, rather than the distance based, similarity measure for clustering; (ii) since the fitness measure is ... See full document
7
A Survey of Clustering Algorithm for Very Large Datasets
... in data mining is viewed as unsupervised method of data ...analysis. Clustering allows users to analyze data from many different dimensions or angles, categorize it, and summarize the ... See full document
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