Steven F. Ashby Center for Applied Scientific Computing
... to each other based on the important terms appearing in them. Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. Gain: Information Retrieval can utilize the clusters to relate a new document ...
... to each other based on the important terms appearing in them. Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. Gain: Information Retrieval can utilize the clusters to relate a new document ...
D - Orca
... classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data ...
... classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data ...
Topic7-TextMining
... • Calculating similarity is not obvious - what is the distance between two sentences or queries? • Evaluating retrieval is hard: what is the “right” answer ? (no ground truth) • User can query things you have not seen before e.g. misspelled, foreign, new terms. • Goal (score function) is different t ...
... • Calculating similarity is not obvious - what is the distance between two sentences or queries? • Evaluating retrieval is hard: what is the “right” answer ? (no ground truth) • User can query things you have not seen before e.g. misspelled, foreign, new terms. • Goal (score function) is different t ...
Density-based Algorithms for Active and Anytime Clustering
... need of new data mining technologies to deal with complex data has emerged during the last decades. In this thesis, we focus on the data mining task of clustering in which objects are separated in different groups (clusters) such that objects inside a cluster are more similar than objects in differe ...
... need of new data mining technologies to deal with complex data has emerged during the last decades. In this thesis, we focus on the data mining task of clustering in which objects are separated in different groups (clusters) such that objects inside a cluster are more similar than objects in differe ...
DBMiner: A System for Data Mining in Relational Databases and
... database. For example, one may discover a set of symptoms often occurring together with certain kinds of diseases and further study the reasons behind them. A meta-pattern guided miner is a data mining mechanism which takes a userspeci ed meta-rule form, such as \P(x; y) Q(y; z) R(x; z)" as a patter ...
... database. For example, one may discover a set of symptoms often occurring together with certain kinds of diseases and further study the reasons behind them. A meta-pattern guided miner is a data mining mechanism which takes a userspeci ed meta-rule form, such as \P(x; y) Q(y; z) R(x; z)" as a patter ...
file (4.3 MB, pdf)
... • Different data-sources tend to have → different conventions for coding information & → different standards for the quality of information • Building an ODS requires data filtering, data cleaning and integration. • Data-errors at least partly arise because of unmotivated data-entry staff. • Success ...
... • Different data-sources tend to have → different conventions for coding information & → different standards for the quality of information • Building an ODS requires data filtering, data cleaning and integration. • Data-errors at least partly arise because of unmotivated data-entry staff. • Success ...
BORDER: Efficient Computation of Boundary Points
... an R2NN of p2 . In contrast, p5 and p7 are close to p2 but they are not answers of the R2NN query of p2 . Moreover, p2 has 3 reverse 2-nearest neighbors while p4 and p8 has 0 reverse 2-nearest neighbor. These properties of RkNN have potential applications in the area of data mining. However, the com ...
... an R2NN of p2 . In contrast, p5 and p7 are close to p2 but they are not answers of the R2NN query of p2 . Moreover, p2 has 3 reverse 2-nearest neighbors while p4 and p8 has 0 reverse 2-nearest neighbor. These properties of RkNN have potential applications in the area of data mining. However, the com ...
Workshop on Ubiquitous Data Mining
... In order to discover characteristic patterns in large spatiotemporal data sets, mining algorithms have to take into account spatial relations, such as topology and direction, as well as temporal relations. The increased use of devices that are capable of storing driving-related spatio-temporal infor ...
... In order to discover characteristic patterns in large spatiotemporal data sets, mining algorithms have to take into account spatial relations, such as topology and direction, as well as temporal relations. The increased use of devices that are capable of storing driving-related spatio-temporal infor ...
1435596563
... In this present study, for modeling user behavior (navigation) on the Web, the use of Markov models is a reasonable choice as they are compact, simple and based on well-established theory. Several Markov models were proposed for modelling user Web data: first-order Markov model, hybrid-order tree-li ...
... In this present study, for modeling user behavior (navigation) on the Web, the use of Markov models is a reasonable choice as they are compact, simple and based on well-established theory. Several Markov models were proposed for modelling user Web data: first-order Markov model, hybrid-order tree-li ...
Third, a data warehouse facilitates customer
... 11. What is MODEL in Data mining world? o Models in Data mining help the different algorithms in decision making or pattern matching. The second stage of data mining involves considering various models and choosing the best one based on their predictive performance. 12. Explain how to mine an OLAP ...
... 11. What is MODEL in Data mining world? o Models in Data mining help the different algorithms in decision making or pattern matching. The second stage of data mining involves considering various models and choosing the best one based on their predictive performance. 12. Explain how to mine an OLAP ...
CS490D: Introduction to Data Mining Chris Clifton
... Safety Board (NTSB) and the Federal Aviation Administration (FAA) • Integrating data from different sources as well as mining for patterns from a mix of both structured fields and free text is a difficult task • The goal of our initial analysis is to determine how data mining can be used to improve ...
... Safety Board (NTSB) and the Federal Aviation Administration (FAA) • Integrating data from different sources as well as mining for patterns from a mix of both structured fields and free text is a difficult task • The goal of our initial analysis is to determine how data mining can be used to improve ...
Contextual Anomaly Detection in Big Sensor Data
... drawbacks. First, it is likely to miss important relationships between similar sensors within the network as point anomaly detectors work on the global view of the data. Second, it is likely to generate a false positive anomaly when context such as the time of day, time of year, or type of location ...
... drawbacks. First, it is likely to miss important relationships between similar sensors within the network as point anomaly detectors work on the global view of the data. Second, it is likely to generate a false positive anomaly when context such as the time of day, time of year, or type of location ...
ASSOCIATION RULE MINING ALGORITHMS FOR HIGH - e
... several algorithms have been developed till today. In a database of transaction D with a set of n binary attributes(items) I , a rule defined as an implication of the form X Y where X,Y Є I and X ∩ Y. where X and Y are called Antecedent and Consequent of the rule respectively. The support, supp(X), ...
... several algorithms have been developed till today. In a database of transaction D with a set of n binary attributes(items) I , a rule defined as an implication of the form X Y where X,Y Є I and X ∩ Y. where X and Y are called Antecedent and Consequent of the rule respectively. The support, supp(X), ...
Data Mining and Knowledge Discovery Handbook
... All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in co ...
... All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in co ...
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.