Using Data Mining for Mobile Communication Clustering and
... observation space. It is also common practice to initialize clusters with members that are most representative of the entire set of observations. The Euclidean distance was chosen to run the algorithm. For a better representation, all the data was scaled in the range [0,1]. To do this scaling, for e ...
... observation space. It is also common practice to initialize clusters with members that are most representative of the entire set of observations. The Euclidean distance was chosen to run the algorithm. For a better representation, all the data was scaled in the range [0,1]. To do this scaling, for e ...
A New Approach for Evaluation of Data Mining Techniques
... several important questions about their data: what patterns are there in database?, what is the chance that an event will occur?, which patterns are significant?, and what is a high level summary of the data that gives some idea of what is contained in database? In statistics, prediction is usually ...
... several important questions about their data: what patterns are there in database?, what is the chance that an event will occur?, which patterns are significant?, and what is a high level summary of the data that gives some idea of what is contained in database? In statistics, prediction is usually ...
Chapter 3: Cluster Analysis
... F and G: different distributions F and G : the same distribution but with different parameters Distribution G must have the potential to produce outliers (a different mean, or dispersion, or a longer tail) ...
... F and G: different distributions F and G : the same distribution but with different parameters Distribution G must have the potential to produce outliers (a different mean, or dispersion, or a longer tail) ...
Incremental spectral clustering by efficiently updating the eigen
... eigenvalue systems. Spectral clustering evolved from the theory of spectral graph partitioning, an effective algorithm in high performance computing [11]. Recently there is a huge volume of literature on this topic. Ratio cut objective function [12,13] naturally captures both mincut and equipartitio ...
... eigenvalue systems. Spectral clustering evolved from the theory of spectral graph partitioning, an effective algorithm in high performance computing [11]. Recently there is a huge volume of literature on this topic. Ratio cut objective function [12,13] naturally captures both mincut and equipartitio ...
4. Conclusions Acknowledgments 5. References
... is based on two lemmata which can also be proven for the generalized notion of a cluster, i.e. a density-connected set. In the current context they state the following. Given the parameters NPred and MinWeight, we can discover a densityconnected set in a two-step approach. First, choose an arbitrary ...
... is based on two lemmata which can also be proven for the generalized notion of a cluster, i.e. a density-connected set. In the current context they state the following. Given the parameters NPred and MinWeight, we can discover a densityconnected set in a two-step approach. First, choose an arbitrary ...
data mining - Department of Information Technology
... Involves the analysis of data and the use of software techniques for finding hidden and unexpected patterns and relationships in sets of data; in contrast to information and knowledge that are already intuitive. Patterns and relationships are identified by examining the underlying rules and features ...
... Involves the analysis of data and the use of software techniques for finding hidden and unexpected patterns and relationships in sets of data; in contrast to information and knowledge that are already intuitive. Patterns and relationships are identified by examining the underlying rules and features ...
Means
... The fundamental reason why the algorithm above is effective in reducing the number of distance calculations is that at the start of each iteration, the upper bounds 70 and the lower bounds C are tight for most points and centers . If these bounds are tight at the start of one iteratio ...
... The fundamental reason why the algorithm above is effective in reducing the number of distance calculations is that at the start of each iteration, the upper bounds 70 and the lower bounds C are tight for most points and centers . If these bounds are tight at the start of one iteratio ...
A General Framework for Mining Massive Data Streams
... high-volume, open-ended data streams we see today, the data mining system should be continuously on, processing records at the speed they arrive, incorporating them into the model it is building even if it never sees them again. A system capable of doing this needs to meet a number of stringent desi ...
... high-volume, open-ended data streams we see today, the data mining system should be continuously on, processing records at the speed they arrive, incorporating them into the model it is building even if it never sees them again. A system capable of doing this needs to meet a number of stringent desi ...
Title: Spatial Data Mining in Geo-Business
... Probability of Visitation (not possible for this demo) ...
... Probability of Visitation (not possible for this demo) ...
S2I2: Enabling grand challenge data intensive problems
... • weighted/unweighted, weight distribution • vertex degree distribution • directed/undirected • simple/multi/hyper graph • problem size • granularity of computation at nodes/edges • domain-specific characteristics ...
... • weighted/unweighted, weight distribution • vertex degree distribution • directed/undirected • simple/multi/hyper graph • problem size • granularity of computation at nodes/edges • domain-specific characteristics ...
Data clustering: 50 years beyond K-means
... 2006); in semi-supervised classification, the labels of only a small portion of the training data set are available. The unlabeled data, instead of being discarded, are also used in the learning process. In semi-supervised clustering, instead of specifying the class labels, pair-wise constraints are ...
... 2006); in semi-supervised classification, the labels of only a small portion of the training data set are available. The unlabeled data, instead of being discarded, are also used in the learning process. In semi-supervised clustering, instead of specifying the class labels, pair-wise constraints are ...
Clustering - Ohio State Computer Science and Engineering
... closer (more similar) to the “center” of a cluster, than to the center of any other cluster – The center of a cluster is often a centroid, the average of all the points in the cluster, or a medoid, the most “representative” point of a cluster ...
... closer (more similar) to the “center” of a cluster, than to the center of any other cluster – The center of a cluster is often a centroid, the average of all the points in the cluster, or a medoid, the most “representative” point of a cluster ...
Text Mining: Finding Nuggets in Mountains of Textual Data
... from a document collection usually is very high, easily running into several thousands. There are two consequences of this affecting the overall text mining process. 1. The feature selection task is quite different, since it is no longer feasible to have a human examine each feature to decide whethe ...
... from a document collection usually is very high, easily running into several thousands. There are two consequences of this affecting the overall text mining process. 1. The feature selection task is quite different, since it is no longer feasible to have a human examine each feature to decide whethe ...
Implementation of Data Mining Techniques for Meteorological Data
... technologies have been elaborated over the last few years, producing a huge amount of data. This huge raw data is difficult to analyze and understand. In this case clustering aim to improve the understanding of natural climate processes, to assess the quality of climate model results and to identify ...
... technologies have been elaborated over the last few years, producing a huge amount of data. This huge raw data is difficult to analyze and understand. In this case clustering aim to improve the understanding of natural climate processes, to assess the quality of climate model results and to identify ...
Cluster Ensembles for High Dimensional Clustering
... Our first ensemble constructor is based on random projection, a dimension reduction technique that has seen growing popularity due to its simplicity and theoretical promise. We will refer to this approach as the RP-based approach and the resulting ensembles as RP ensembles. Our motivation for using ...
... Our first ensemble constructor is based on random projection, a dimension reduction technique that has seen growing popularity due to its simplicity and theoretical promise. We will refer to this approach as the RP-based approach and the resulting ensembles as RP ensembles. Our motivation for using ...
Clustering and Approximate Identification of Frequent Item Sets
... The identification of frequent item sets and of association rules is a major research direction in data mining that has been examined in a large number of publications. In (Afrati, Gionis, & Mannila 2004) it is shown that most variants of concise approximations of collections of frequent item sets a ...
... The identification of frequent item sets and of association rules is a major research direction in data mining that has been examined in a large number of publications. In (Afrati, Gionis, & Mannila 2004) it is shown that most variants of concise approximations of collections of frequent item sets a ...
Clinical Decision Support Systems for Heart Disease Using Data
... inherently complex, particularly as overall treatment frequently involves multiple, often concurrent, elements. This complexity makes treatment recommendation from data mining very difficult. Data mining is suited to assist decision making when many variables must be assessed, such as multiple concu ...
... inherently complex, particularly as overall treatment frequently involves multiple, often concurrent, elements. This complexity makes treatment recommendation from data mining very difficult. Data mining is suited to assist decision making when many variables must be assessed, such as multiple concu ...
k - Computer Science
... • with heavy-tailed data, e.g., when the magnitude of the elements of the feature vector decay in a heavy-tailed manner ...
... • with heavy-tailed data, e.g., when the magnitude of the elements of the feature vector decay in a heavy-tailed manner ...
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.