Welcoming Remarks
... Clustering with Bregman Divergences, Arindam Banerjee (Univ. of Texas, Austin), Srujana Merugu (Univ. of Texas, Austin), Inderjit Dhillon (Univ. of Texas, Austin), Joydeep Ghosh (Univ. of Texas) Probabilistic/statistical Methods I (Friday, 10:00am) Best Applications Paper: Enhancing Communities of I ...
... Clustering with Bregman Divergences, Arindam Banerjee (Univ. of Texas, Austin), Srujana Merugu (Univ. of Texas, Austin), Inderjit Dhillon (Univ. of Texas, Austin), Joydeep Ghosh (Univ. of Texas) Probabilistic/statistical Methods I (Friday, 10:00am) Best Applications Paper: Enhancing Communities of I ...
Identifying and Removing, Irrelevant and Redundant
... As distributional clustering of words are agglomerative in nature, and result in sub-optimal word clusters and high computational cost, Dhillon et al. [18] proposed a new information-theoretic divisive algorithm for word clustering and applied it to text classification. Butterworth et proposed to cl ...
... As distributional clustering of words are agglomerative in nature, and result in sub-optimal word clusters and high computational cost, Dhillon et al. [18] proposed a new information-theoretic divisive algorithm for word clustering and applied it to text classification. Butterworth et proposed to cl ...
Subspace Clustering of High-Dimensional Data: An Evolutionary
... tendency, because such features cause the algorithm to search for clusters where there is no existence of clusters. This also happens with low-dimensional data, but the likelihood of presence of irrelevant features and their number grow with dimension. The second problem is the so-called “curse of d ...
... tendency, because such features cause the algorithm to search for clusters where there is no existence of clusters. This also happens with low-dimensional data, but the likelihood of presence of irrelevant features and their number grow with dimension. The second problem is the so-called “curse of d ...
YADING: Fast Clustering of Large-Scale Time Series Data
... manual specification of k as the number of clusters. k-means and kmedoid are typical partitioning algorithms. CLARANS [19] is an improved k-medoid method, and it is more effective and efficient. Density-based algorithms [22] treat clusters as dense regions of objects in the spatial space separated b ...
... manual specification of k as the number of clusters. k-means and kmedoid are typical partitioning algorithms. CLARANS [19] is an improved k-medoid method, and it is more effective and efficient. Density-based algorithms [22] treat clusters as dense regions of objects in the spatial space separated b ...
Lecture slides - Dataverse
... • What is Data Mining and what it has to do with the World-History Dataverse? – Side show? – Afterthought? – Should we forget about it? ...
... • What is Data Mining and what it has to do with the World-History Dataverse? – Side show? – Afterthought? – Should we forget about it? ...
ANALYSIS OF INDIAN WEATHER DATA SETS USING DATA
... and branches represent conjunctions of features that lead to those class labels. In data mining, a decision tree describes data but not decisions; rather the resulting classification tree can be an input for decision making. J48 are the improved versions of C4.5 algorithms or can be called as optimi ...
... and branches represent conjunctions of features that lead to those class labels. In data mining, a decision tree describes data but not decisions; rather the resulting classification tree can be an input for decision making. J48 are the improved versions of C4.5 algorithms or can be called as optimi ...
Knowledge Discovery and Data Mining
... area. This course provides an overview of Knowledge Discovery and Data Mining (KDD). KDD deals with data integration techniques and with the discovery, interpretation and visualization of patterns in large collections of data. Topics covered in this course include data mining methods such as rule-ba ...
... area. This course provides an overview of Knowledge Discovery and Data Mining (KDD). KDD deals with data integration techniques and with the discovery, interpretation and visualization of patterns in large collections of data. Topics covered in this course include data mining methods such as rule-ba ...
CLARANS: a method for clustering objects for spatial data mining
... approach is to determine a representative object for each cluster. This representative object, called a medoid, is meant to be the most centrally located object within the cluster. Once the medoids have been selected, each nonselected object is grouped with the medoid to which it is the most similar ...
... approach is to determine a representative object for each cluster. This representative object, called a medoid, is meant to be the most centrally located object within the cluster. Once the medoids have been selected, each nonselected object is grouped with the medoid to which it is the most similar ...
Application of Fuzzy Classification in Bankruptcy Prediction Zijiang Yang and Guojun Gan
... intelligent techniques our study will take a new attempt at subspace clustering algorithm and alter it and adapt for the purpose of accurate prediction. This paper modifies a fuzzy subspace clustering (FSC) algorithm to a classification algorithm and applies the resulted classification algorithm to ban ...
... intelligent techniques our study will take a new attempt at subspace clustering algorithm and alter it and adapt for the purpose of accurate prediction. This paper modifies a fuzzy subspace clustering (FSC) algorithm to a classification algorithm and applies the resulted classification algorithm to ban ...
Combining Multiple Clusterings Using Evidence Accumulation
... representation, such as using sub-sets of features; (c) perturbing the data, such as in bootstrapping techniques (like bagging), or sampling approaches, as, for instance, using a set of prototype samples to represent huge data sets. In the second approach, we can generate clustering ensembles by: ( ...
... representation, such as using sub-sets of features; (c) perturbing the data, such as in bootstrapping techniques (like bagging), or sampling approaches, as, for instance, using a set of prototype samples to represent huge data sets. In the second approach, we can generate clustering ensembles by: ( ...
Multi-Step Density-Based Clustering
... Using Multiple Similarity Queries. In [11] a schema was presented which transforms query intensive KDD algorithms into a representation using the similarity join as a basic operation without affecting the correctness of the result of the considered algorithm. The approach was applied to accelerate t ...
... Using Multiple Similarity Queries. In [11] a schema was presented which transforms query intensive KDD algorithms into a representation using the similarity join as a basic operation without affecting the correctness of the result of the considered algorithm. The approach was applied to accelerate t ...
Final Review and Study Guide
... • In the future, for any recurring tasks, please try your best to design the third level of strategy. If you can make it, you can get success in your area. This is not a dream or legend, but the smartest choice very few people ...
... • In the future, for any recurring tasks, please try your best to design the third level of strategy. If you can make it, you can get success in your area. This is not a dream or legend, but the smartest choice very few people ...
Lecture 1: Overview
... length/width ratio is enough. Why should we care how many teeth each kind of fish have, or what shape fins they have? ...
... length/width ratio is enough. Why should we care how many teeth each kind of fish have, or what shape fins they have? ...
Privacy-Preserving Data Visualization using Parallel Coordinates
... • Seeding stage. Given a set of n records in screen space, we choose the best one to start clustering with. This is often done randomly, but we use the axis histograms described above to start in areas of maximum density. • Cluster stage. The next record is chosen in such a way that the chosen cost ...
... • Seeding stage. Given a set of n records in screen space, we choose the best one to start clustering with. This is often done randomly, but we use the axis histograms described above to start in areas of maximum density. • Cluster stage. The next record is chosen in such a way that the chosen cost ...
A Multi-Resolution Clustering Approach for Very Large Spatial
... Grid-based method (STING) for spatial data mining [WYM97]. They divide the spatial area into rectangular cells using a hierarchical structure. They store the statistical parameters (such as mean, variance, minimum, maximum, and type of distribution) of each numerical feature of the objects within ce ...
... Grid-based method (STING) for spatial data mining [WYM97]. They divide the spatial area into rectangular cells using a hierarchical structure. They store the statistical parameters (such as mean, variance, minimum, maximum, and type of distribution) of each numerical feature of the objects within ce ...
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.