Partitioning clustering algorithms for protein sequence data sets
... fact, the number of protein sequences available now is very important (in the order of millions) and hierarchical methods are computationally very expensive so they cannot be extended to cluster large protein sets. However, partitioning methods are very simple and more appropriate to cluster large d ...
... fact, the number of protein sequences available now is very important (in the order of millions) and hierarchical methods are computationally very expensive so they cannot be extended to cluster large protein sets. However, partitioning methods are very simple and more appropriate to cluster large d ...
CHAMELEON: A Hierarchical Clustering Algorithm Using
... (a) for sample size up to 2000 , clusters found poor quality (b) from 2500 sample points and above , about 2.5% of the data set size , CURE always correctly find the clusters. CHAMELEON ...
... (a) for sample size up to 2000 , clusters found poor quality (b) from 2500 sample points and above , about 2.5% of the data set size , CURE always correctly find the clusters. CHAMELEON ...
Time-focused density-based clustering of trajectories of
... together objects which are likely to be generated from a common core trajectory by adding Gaussian noise. In a successive work [4] spatial and (discrete) temporal shifting of trajectories within clusters is also considered. Spatio-temporal density. The problem of finding densely populated regions i ...
... together objects which are likely to be generated from a common core trajectory by adding Gaussian noise. In a successive work [4] spatial and (discrete) temporal shifting of trajectories within clusters is also considered. Spatio-temporal density. The problem of finding densely populated regions i ...
The k-means algorithm
... The history of k-means type of algorithms (LBG Algorithm, 1980) R.M. Gray and D.L. Neuhoff, "Quantization," IEEE Transactions on ...
... The history of k-means type of algorithms (LBG Algorithm, 1980) R.M. Gray and D.L. Neuhoff, "Quantization," IEEE Transactions on ...
Finding Similar Patterns in Microarray Data
... expression data. Most clustering models [4, 1, 8, 10, 7, 9] are distance based clusterings such as Euclidean distance and cosine distance. However, these similarity functions are not always sufficient in capturing correlations among genes or conditions. To remedy this problem,the bicluster model [2] u ...
... expression data. Most clustering models [4, 1, 8, 10, 7, 9] are distance based clusterings such as Euclidean distance and cosine distance. However, these similarity functions are not always sufficient in capturing correlations among genes or conditions. To remedy this problem,the bicluster model [2] u ...
Minimum Entropy Clustering and Applications to Gene Expression
... e.g. hierarchical clustering and EM algorithm. For our purpose, however, it is adequate enough. Besides analyzing gene expression data, clustering can also be applied to many other problems, including statistical data analysis, data mining, compression, vector quantization, etc. As a branch of stati ...
... e.g. hierarchical clustering and EM algorithm. For our purpose, however, it is adequate enough. Besides analyzing gene expression data, clustering can also be applied to many other problems, including statistical data analysis, data mining, compression, vector quantization, etc. As a branch of stati ...
A Comparative Study of Issues in Big Data Clustering Algorithm with
... two clusters are chosen and merged. This process continuous until n clusters is generated. While hierarchical methods have been successfully applied to many biological applications, they are well known to suffer from the weakness that they can never undo what was done previously. Once an agglomerati ...
... two clusters are chosen and merged. This process continuous until n clusters is generated. While hierarchical methods have been successfully applied to many biological applications, they are well known to suffer from the weakness that they can never undo what was done previously. Once an agglomerati ...
A new hybrid method based on partitioning
... the database. Clustering procedure partition a set of data objects into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some predefined criteria (Güngör & Ünler, 2007). These data objects are also called data points or poin ...
... the database. Clustering procedure partition a set of data objects into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some predefined criteria (Güngör & Ünler, 2007). These data objects are also called data points or poin ...
Pattern Recognition, Data Mining, and Image Processing for
... Classification, data clustering, regression, sequence labeling, and parsing, which assigns a parse tree to an input sentence, are some pattern recognition methods. Hence, because of its capability of discovering patterns from data, there is an increasing need to do more research in the area of patte ...
... Classification, data clustering, regression, sequence labeling, and parsing, which assigns a parse tree to an input sentence, are some pattern recognition methods. Hence, because of its capability of discovering patterns from data, there is an increasing need to do more research in the area of patte ...
CSE/CIS 787: Analytical Data Mining
... 2. CRISP model; description of each phase. 3. Description of the four functionalities in DM; classification; prediction; association rules; clustering. PartB: Classification (similar to assignment No.2) (50%) 1. Classification, 2-steps of development and use of classification model 2. Decision tree ...
... 2. CRISP model; description of each phase. 3. Description of the four functionalities in DM; classification; prediction; association rules; clustering. PartB: Classification (similar to assignment No.2) (50%) 1. Classification, 2-steps of development and use of classification model 2. Decision tree ...
Compiler Techniques for Data Parallel Applications With Very Large
... Real time requirement on processing rate – tradeoffs between accuracy of analysis and efficiency Placement of data – obviously want to process an individual stream close to the source of data Feedback based control of accuracy – cannot allow any computational or communication stage to become the bot ...
... Real time requirement on processing rate – tradeoffs between accuracy of analysis and efficiency Placement of data – obviously want to process an individual stream close to the source of data Feedback based control of accuracy – cannot allow any computational or communication stage to become the bot ...
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.