cs-171-21a-Clustering_reza_asadi
... • Cost function for clustering is : what is the optimal value of k? (can increasing k ever increase the cost?) • This is a model complexity issue – Much like choosing lots of features – they only (seem to) help – Too many clusters leads to over fitting. • To reduce the number of clusters, one soluti ...
... • Cost function for clustering is : what is the optimal value of k? (can increasing k ever increase the cost?) • This is a model complexity issue – Much like choosing lots of features – they only (seem to) help – Too many clusters leads to over fitting. • To reduce the number of clusters, one soluti ...
2008 Midterm Exam
... The new set of representatives is chosen from the following set: {R1, R3, R4, R6}. Q4) (3pts) The framework finds interesting places and their associated patterns in spatial datasets. The interestingness is captured by a reward-based fitness function; the fitness function captures what the domain ex ...
... The new set of representatives is chosen from the following set: {R1, R3, R4, R6}. Q4) (3pts) The framework finds interesting places and their associated patterns in spatial datasets. The interestingness is captured by a reward-based fitness function; the fitness function captures what the domain ex ...
A cluster is considered to be stable depending on stability value
... Work to be done: Testing of different types of clustering algorithms and calculate their performances, complexity in a system. Testing of clustering algorithms in grid environment and measure their performances to find out the stable clustering algorithm. Finally, implementation of the stable ...
... Work to be done: Testing of different types of clustering algorithms and calculate their performances, complexity in a system. Testing of clustering algorithms in grid environment and measure their performances to find out the stable clustering algorithm. Finally, implementation of the stable ...
PPT - Computer Science
... Applicable only when mean is defined, then what about categorical data? Need to specify k, the number of clusters, in advance Unable to handle noisy data and outliers Not suitable to discover clusters with non-convex shapes ...
... Applicable only when mean is defined, then what about categorical data? Need to specify k, the number of clusters, in advance Unable to handle noisy data and outliers Not suitable to discover clusters with non-convex shapes ...
Data Mining with Oracle using Classification and Clustering Algorithms
... Investigate two types of algorithms available in Oracle10g for data mining (ODM). ...
... Investigate two types of algorithms available in Oracle10g for data mining (ODM). ...
Density Based Clustering - DBSCAN [Modo de Compatibilidade]
... DBSCAN can only result in a good clustering as good as its distance measure is in the function getNeighbors(P,epsilon). The most common distance metric used is the euclidean distance measure. Especially for high-dimensional data, this distance metric can be rendered almost useless due to the so call ...
... DBSCAN can only result in a good clustering as good as its distance measure is in the function getNeighbors(P,epsilon). The most common distance metric used is the euclidean distance measure. Especially for high-dimensional data, this distance metric can be rendered almost useless due to the so call ...
Clustering Time Series Data An Evolutionary
... the clustering performed on many individual time series to group similar series into clusters based on sliding window extractions of a single time series and aims to find similarity and differences among different time window of a single time series. ...
... the clustering performed on many individual time series to group similar series into clusters based on sliding window extractions of a single time series and aims to find similarity and differences among different time window of a single time series. ...
Clustering178winter07
... • Often initialization is very important since there are very many local minima in C. Relatively good initialization: place cluster locations on K randomly chosen data-cases. • How to choose K? Add complexity term: C C 1 [# parameters ] log(N ) ...
... • Often initialization is very important since there are very many local minima in C. Relatively good initialization: place cluster locations on K randomly chosen data-cases. • How to choose K? Add complexity term: C C 1 [# parameters ] log(N ) ...
Data Mining
... • Strength: Relatively efficient: O(tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t << n. • Comparing: PAM: O(k(n-k)2 ), CLARA: O(ks2 + k(n-k)) • Comment: Often terminates at a local optimum. The global optimum may be found using techniques such as: deterministic a ...
... • Strength: Relatively efficient: O(tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t << n. • Comparing: PAM: O(k(n-k)2 ), CLARA: O(ks2 + k(n-k)) • Comment: Often terminates at a local optimum. The global optimum may be found using techniques such as: deterministic a ...
Clustering - Hong Kong University of Science and Technology
... Clustering based on density (local cluster criterion), such as density-connected points ...
... Clustering based on density (local cluster criterion), such as density-connected points ...
EM Algorithm
... respect to Q (theta fixed) and then maximizing F with respect to theta (Q fixed). ...
... respect to Q (theta fixed) and then maximizing F with respect to theta (Q fixed). ...
Clustering
... Relatively efficient: O(tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t << n. Often terminates at a local optimum. The global optimum may be found using techniques such as: deterministic annealing and genetic algorithms Weakness Applicable only when mean is d ...
... Relatively efficient: O(tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t << n. Often terminates at a local optimum. The global optimum may be found using techniques such as: deterministic annealing and genetic algorithms Weakness Applicable only when mean is d ...
Classification Under the Relevant Set Correlation Model
... the relevant-set correlation (RSC) clustering model [1] as a means of determining the most appropriate set of voting neighbors. Developed at NII, RSC is a generic model for clustering that requires no direct knowledge of the nature or representation of the data, but instead relies solely on the rank ...
... the relevant-set correlation (RSC) clustering model [1] as a means of determining the most appropriate set of voting neighbors. Developed at NII, RSC is a generic model for clustering that requires no direct knowledge of the nature or representation of the data, but instead relies solely on the rank ...
Discovery of Climate Indices using Clustering
... – They are well-accepted by Earth scientists. – They are related to well-known climate phenomena such as El Nino. ...
... – They are well-accepted by Earth scientists. – They are related to well-known climate phenomena such as El Nino. ...
No Slide Title - The University of North Carolina at Chapel Hill
... Decompose data objects into a multi-level nested partitioning (a tree of clusters) A clustering of the data objects: cutting the dendrogram at the desired level Each connected component forms a cluster ...
... Decompose data objects into a multi-level nested partitioning (a tree of clusters) A clustering of the data objects: cutting the dendrogram at the desired level Each connected component forms a cluster ...
Document
... E.g.: A traffic jam along a road It should be represented as a cluster which individuals form a “snake-shaped” cluster ...
... E.g.: A traffic jam along a road It should be represented as a cluster which individuals form a “snake-shaped” cluster ...
pr10part2_ding
... samples into c clusters • The first is a partition into n cluster, each one containing exactly one sample • The second is a partition into n-1 clusters, the third into n-2, and so on, until the n-th in which there is only one cluster containing all of the samples • At the level k in the sequence, c ...
... samples into c clusters • The first is a partition into n cluster, each one containing exactly one sample • The second is a partition into n-1 clusters, the third into n-2, and so on, until the n-th in which there is only one cluster containing all of the samples • At the level k in the sequence, c ...
Calling Polyploid Genotypes with GenoStudio Software v2010.3/v1.8
... Project Options Dialog Box is available through the Tools Menu within the GenomeStudio Genotyping Module (Figure 1). Options can be adjusted per project to increase or decrease the algorithm sensitivity to cluster detection by adjusting minimum number of points required to define a cluster and defau ...
... Project Options Dialog Box is available through the Tools Menu within the GenomeStudio Genotyping Module (Figure 1). Options can be adjusted per project to increase or decrease the algorithm sensitivity to cluster detection by adjusting minimum number of points required to define a cluster and defau ...
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.