Towards Effective and Efficient Distributed Clustering
... The transmission of huge amount of data from one site to another central site is in some application areas almost impossible. In astronomy, for instance, there exist several high sophisticated space telescopes spread all over the world. These telescopes gather data unceasingly. Each of them is able ...
... The transmission of huge amount of data from one site to another central site is in some application areas almost impossible. In astronomy, for instance, there exist several high sophisticated space telescopes spread all over the world. These telescopes gather data unceasingly. Each of them is able ...
A Novel Path-Based Clustering Algorithm Using Multi
... of patterns, points or objects [1]. The clustering task plays a very important role in many areas such as exploratory data analysis, pattern recognition, computer vision, and information retrieval. Although cluster analysis has a long history, there are still many challenges, and the goal of designi ...
... of patterns, points or objects [1]. The clustering task plays a very important role in many areas such as exploratory data analysis, pattern recognition, computer vision, and information retrieval. Although cluster analysis has a long history, there are still many challenges, and the goal of designi ...
PPT - pantherFILE
... the hidden nodes to the input nodes. – At each level, the link weights between nodes are updated to avoid similar mistake – Different algorithms were developed • Gradient descent • Newton’s method • Genetic algorithms ...
... the hidden nodes to the input nodes. – At each level, the link weights between nodes are updated to avoid similar mistake – Different algorithms were developed • Gradient descent • Newton’s method • Genetic algorithms ...
Improved Clustering And Naïve Bayesian Based Binary Decision
... data analysis that arises in many applications in numerous fields such as data mining[3], image processing, machine learning and bioinformatics. Since, in fact its's an unsupervised learning method, it does not need train datasets and pre-defined taxonomies. Fact is that there are several special re ...
... data analysis that arises in many applications in numerous fields such as data mining[3], image processing, machine learning and bioinformatics. Since, in fact its's an unsupervised learning method, it does not need train datasets and pre-defined taxonomies. Fact is that there are several special re ...
Multiobjective Clustering with Automatic k
... Apply MOCK to web data clustering with a scalable automatic k-determination scheme. Determine the appropriate k at low cost. ...
... Apply MOCK to web data clustering with a scalable automatic k-determination scheme. Determine the appropriate k at low cost. ...
CSGA 6950 - Fordham University
... This course will cover data mining and machine learning algorithms for analyzing large data sets as well as the practical issues that arise when applying these algorithms to real-world problems. It will balance theory and practice—the principles of data mining methods will be discussed but students ...
... This course will cover data mining and machine learning algorithms for analyzing large data sets as well as the practical issues that arise when applying these algorithms to real-world problems. It will balance theory and practice—the principles of data mining methods will be discussed but students ...
Clustering census data: comparing the performance of
... early stages of the process. This characteristic can be seen as an “annealing schedule” which provides an early exploration of the search space [36]. On the other hand, k-means gradient orientation forces a premature convergence which, depending on the initialization, may frequently yield local opti ...
... early stages of the process. This characteristic can be seen as an “annealing schedule” which provides an early exploration of the search space [36]. On the other hand, k-means gradient orientation forces a premature convergence which, depending on the initialization, may frequently yield local opti ...
Sl1 - Maastricht University
... of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. – Approach: • Collect different attributes of customers based on their geographical and lifestyle related information. • Find clusters of similar customers. • Measure the cluster ...
... of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. – Approach: • Collect different attributes of customers based on their geographical and lifestyle related information. • Find clusters of similar customers. • Measure the cluster ...
Title Distributed Clustering Algorithm for Spatial Data Mining Author(s)
... clusters (models). During the second phase communicating the local clusters to the heads may generate huge overhead. Therefore, the objective is to minimise the data communication and computational time, while getting accurate global results. Note that our approach while it is based on the same prin ...
... clusters (models). During the second phase communicating the local clusters to the heads may generate huge overhead. Therefore, the objective is to minimise the data communication and computational time, while getting accurate global results. Note that our approach while it is based on the same prin ...
Title Distributed Clustering Algorithm for Spatial Data Mining Author(s)
... clusters (models). During the second phase communicating the local clusters to the heads may generate huge overhead. Therefore, the objective is to minimise the data communication and computational time, while getting accurate global results. Note that our approach while it is based on the same prin ...
... clusters (models). During the second phase communicating the local clusters to the heads may generate huge overhead. Therefore, the objective is to minimise the data communication and computational time, while getting accurate global results. Note that our approach while it is based on the same prin ...
Document
... Phase 1: scan DB to build an initial in-memory CF tree (a multi-level compression of the data that tries to preserve the inherent clustering structure of the data) ...
... Phase 1: scan DB to build an initial in-memory CF tree (a multi-level compression of the data that tries to preserve the inherent clustering structure of the data) ...
An Efficient Supervised Document Clustering
... The problem of clustering has been studied widely in the database and statistics literature in the context of a wide variety of data mining tasks. The clustering problem is defined to be that of finding groups of similar objects in the data. The similarity between the objects is measured with the us ...
... The problem of clustering has been studied widely in the database and statistics literature in the context of a wide variety of data mining tasks. The clustering problem is defined to be that of finding groups of similar objects in the data. The similarity between the objects is measured with the us ...
K-Means Based Clustering In High Dimensional Data
... In general, most of the clustering algorithms cannot create correct results because of the inherent sparsity of the data space. High dimensional data does not cluster large distance. But clusters in lower dimensional subspaces are easily use. In this paper, they present a preprocessing step for clus ...
... In general, most of the clustering algorithms cannot create correct results because of the inherent sparsity of the data space. High dimensional data does not cluster large distance. But clusters in lower dimensional subspaces are easily use. In this paper, they present a preprocessing step for clus ...
H 566 Data Mining Syllabus
... Provide clear and concise interpretations and written and oral presentations of an analysis of a high-dimensional data set arising in a biological, medical, or public health application using at least one modern technique. Course Content: This course is designed as a survey of and introduction to hi ...
... Provide clear and concise interpretations and written and oral presentations of an analysis of a high-dimensional data set arising in a biological, medical, or public health application using at least one modern technique. Course Content: This course is designed as a survey of and introduction to hi ...
Definitions of Data Mining
... Make a paper on one of the topics : The manager of any company may ask his workers what our costumers mostly buy in Gaza and in Kahn Younis. Probably this kind of question needs to discover the knowledge which is stored in the database and requires a complex SQL statement. Definitions of Data Mining ...
... Make a paper on one of the topics : The manager of any company may ask his workers what our costumers mostly buy in Gaza and in Kahn Younis. Probably this kind of question needs to discover the knowledge which is stored in the database and requires a complex SQL statement. Definitions of Data Mining ...
An Educational Data Mining System for Advising Higher Education
... importance in the education domain. M. Sukanya, S. Biruntha, S. Karthik and T. Kalaikumaran [4] applied the Bayesian classification technique on the existing higher education student. The main goal of their study is to predict the number of upcoming students in the next year based on the valid numbe ...
... importance in the education domain. M. Sukanya, S. Biruntha, S. Karthik and T. Kalaikumaran [4] applied the Bayesian classification technique on the existing higher education student. The main goal of their study is to predict the number of upcoming students in the next year based on the valid numbe ...
Web Mining (網路探勘)
... (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on – Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) – Fuzzy logic (e.g., fuzzy c-means algorithm) – Genetic algorithms ...
... (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on – Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) – Fuzzy logic (e.g., fuzzy c-means algorithm) – Genetic algorithms ...
Clustering of Low-Level Acoustic Features Extracted
... framing. A 44100 kbps stereo audio signal has 44100 samples per second for left and right channels separately. We have to specify a frame size that is sufficient to capture the smallest significant sound (minimum length note) in the song. Since we did not focus on obtaining a beat histogram in our w ...
... framing. A 44100 kbps stereo audio signal has 44100 samples per second for left and right channels separately. We have to specify a frame size that is sufficient to capture the smallest significant sound (minimum length note) in the song. Since we did not focus on obtaining a beat histogram in our w ...
Data Mining
... This course introduces basic concepts, tasks, methods, and techniques in Data Mining. The emphasis is on various Data Mining problems and their solutions. Students will develop an understanding of the Data Mining and issues, learn various techniques for Data Mining, and apply the techniques in solvi ...
... This course introduces basic concepts, tasks, methods, and techniques in Data Mining. The emphasis is on various Data Mining problems and their solutions. Students will develop an understanding of the Data Mining and issues, learn various techniques for Data Mining, and apply the techniques in solvi ...
Clustering
... Birch – Phase 1 • Start with initial threshold and insert points into the tree • If run out of memory, increase thresholdvalue, and rebuild a smaller tree by reinserting values from older tree and then other values • Good initial threshold is important but hard to figure out • Outlier removal – whe ...
... Birch – Phase 1 • Start with initial threshold and insert points into the tree • If run out of memory, increase thresholdvalue, and rebuild a smaller tree by reinserting values from older tree and then other values • Good initial threshold is important but hard to figure out • Outlier removal – whe ...
AST 4031 Syllabus (updated) (pdf)
... The first part of the course will cover probability theory and the foundation of statistical inference: • overview of probability and random variables • discrete and continuous distributions • limit theorems • Concepts of statistical inference: classical vs. Bayesian statistical inference • Maximum ...
... The first part of the course will cover probability theory and the foundation of statistical inference: • overview of probability and random variables • discrete and continuous distributions • limit theorems • Concepts of statistical inference: classical vs. Bayesian statistical inference • Maximum ...
Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς ""grape"") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.