Building Data Cubes and Mining Them
... Imagine that you are a database administrator working for the FoodMart corporation. FoodMart is a large grocery store chain with sales in the United States, Mexico, and Canada. The marketing department wants to analyze all of the sales by products and customers that were made during the 2009 calenda ...
... Imagine that you are a database administrator working for the FoodMart corporation. FoodMart is a large grocery store chain with sales in the United States, Mexico, and Canada. The marketing department wants to analyze all of the sales by products and customers that were made during the 2009 calenda ...
An Accelerated MapReduce-based K
... Big data clustering has recently received a lot of attentions to build parallel clustering methods. In this context, several parallel clustering methods have been designed in the literature [2, 4, 10, 15, 17, 18, 20, 24]. Most of these methods use the MapReduce [5], which is a programming model for ...
... Big data clustering has recently received a lot of attentions to build parallel clustering methods. In this context, several parallel clustering methods have been designed in the literature [2, 4, 10, 15, 17, 18, 20, 24]. Most of these methods use the MapReduce [5], which is a programming model for ...
![ZRL96] Tian Zhang, Raghu Ramakrishnan, and Miron Livny. Birch](http://s1.studyres.com/store/data/000747640_1-addf9ab4ee746db8d51240a091979a0d-300x300.png)
ZRL96] Tian Zhang, Raghu Ramakrishnan, and Miron Livny. Birch
... missing from the data set. This makes it dicult to use the traditional algorithm since it is unclear as to how to treat the missing values in the context of traditional hierarchical clustering. The result of our clustering algorithm with = 0:8 is presented in Table 7. The mutual fund data set is ...
... missing from the data set. This makes it dicult to use the traditional algorithm since it is unclear as to how to treat the missing values in the context of traditional hierarchical clustering. The result of our clustering algorithm with = 0:8 is presented in Table 7. The mutual fund data set is ...
Data Mining for Intrusion Detection: from Outliers to True
... The request will have the following form: staff.php?FName=John\&LName=Doe \&room=204\&floor=2\&Dpt=RD. This new request, due to the recent recruitment of John Due in this department, should not be considered as an attack. On the other hand, let us consider Ay , an anomaly that corresponds to a true ...
... The request will have the following form: staff.php?FName=John\&LName=Doe \&room=204\&floor=2\&Dpt=RD. This new request, due to the recent recruitment of John Due in this department, should not be considered as an attack. On the other hand, let us consider Ay , an anomaly that corresponds to a true ...
77. diffused kernel dmmi approach for theoretic clustering using data
... Abstract— Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Data mining and knowledge discovery in databases (KDD) are treated as synonyms. Knowledge discovery in databases (KDD) is a research area that considers the analysis of la ...
... Abstract— Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Data mining and knowledge discovery in databases (KDD) are treated as synonyms. Knowledge discovery in databases (KDD) is a research area that considers the analysis of la ...
Clustering
... Find homogeneous groups of similar CAD parts Determine standard parts for each group Use standard parts instead of special parts reduction of the number of parts to be produced ...
... Find homogeneous groups of similar CAD parts Determine standard parts for each group Use standard parts instead of special parts reduction of the number of parts to be produced ...
1. Introduction Data mining (DM) is an interdisciplinary field in
... Data mining (DM) is an interdisciplinary field including various scientific disciplines such as: database systems, statistics, machine learning, artificial intelligence and the others [1]. The main goal of data mining is the knowledge exploration from large data sets. The methods of data mining give ...
... Data mining (DM) is an interdisciplinary field including various scientific disciplines such as: database systems, statistics, machine learning, artificial intelligence and the others [1]. The main goal of data mining is the knowledge exploration from large data sets. The methods of data mining give ...
Chapter 11 Statistical Method
... The EM (expectation-maximization) algorithm is a statistical technique that makes use of the finite Gaussian mixtures model. The mixtures model assigns each individual data instance a probability that it would have a certain set of attribute values given it was a member of a specified cluster. The m ...
... The EM (expectation-maximization) algorithm is a statistical technique that makes use of the finite Gaussian mixtures model. The mixtures model assigns each individual data instance a probability that it would have a certain set of attribute values given it was a member of a specified cluster. The m ...
Slide 1
... documents; the position of the text does not matter • What should be the value of k? – What would large or small k mean? ...
... documents; the position of the text does not matter • What should be the value of k? – What would large or small k mean? ...
Document
... documents; the position of the text does not matter • What should be the value of k? – What would large or small k mean? ...
... documents; the position of the text does not matter • What should be the value of k? – What would large or small k mean? ...
Nearest Neighbour - University of Houston
... • Sinkkonen’s [SKN02] discriminative clustering and Tishby’s information bottleneck method [TPB99, ST99] can be viewed as probabilistic supervised clustering algorithms. • There has been a lot of work in the area of semisupervised clustering that centers on clustering with background information. Al ...
... • Sinkkonen’s [SKN02] discriminative clustering and Tishby’s information bottleneck method [TPB99, ST99] can be viewed as probabilistic supervised clustering algorithms. • There has been a lot of work in the area of semisupervised clustering that centers on clustering with background information. Al ...
Pattern Discovery in Hydrological Time Series Data Mining during
... objectively organizing data into homogeneous groups where the within- group-object similarity is minimized and the between-group-object dissimilarity is maximized [21]. The clustering is defined as process of organizing objects into groups whose members are similar in some way. In other way, Cluster ...
... objectively organizing data into homogeneous groups where the within- group-object similarity is minimized and the between-group-object dissimilarity is maximized [21]. The clustering is defined as process of organizing objects into groups whose members are similar in some way. In other way, Cluster ...
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.