PPT
... different attributes of customers based on their geographical and lifestyle related information. Find clusters of similar customers. Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters. ...
... different attributes of customers based on their geographical and lifestyle related information. Find clusters of similar customers. Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters. ...
Data Integration for Homeland Security
... information about how various subtypes of organisms evolved This information is useful when studying disease causing organisms such as viruses and bacteria, because genetically similar types should behave in similar ways ...
... information about how various subtypes of organisms evolved This information is useful when studying disease causing organisms such as viruses and bacteria, because genetically similar types should behave in similar ways ...
Data Mining
... customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. – Approach: Collect ...
... customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. – Approach: Collect ...
A Survey on Clustering Based Feature Selection Technique
... tries to group a set of points into clusters such that points in the same cluster are more similar to each other than points in different clusters, under a particular similarity matrix. Feature subset selection can be viewed as the process of identifying and removing as many irrelevant and redundant ...
... tries to group a set of points into clusters such that points in the same cluster are more similar to each other than points in different clusters, under a particular similarity matrix. Feature subset selection can be viewed as the process of identifying and removing as many irrelevant and redundant ...
03.DataMining_Lec_2.1
... unlikely to trust the results of any data mining that has been applied. Furthermore, dirty data can cause confusion for the mining procedure, resulting in unreliable output. Although most mining routines have some procedures for dealing with incomplete or noisy data, they are not always robust. Inst ...
... unlikely to trust the results of any data mining that has been applied. Furthermore, dirty data can cause confusion for the mining procedure, resulting in unreliable output. Although most mining routines have some procedures for dealing with incomplete or noisy data, they are not always robust. Inst ...
Data Mining - University College Dublin
... Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories ...
... Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories ...
Affiliated Colleges
... Basic data mining tasks – data mining versus knowledge discovery in databases – data mining issues – data mining metrics – social implications of data mining – data mining from a database perspective. Data mining techniques: Introduction – a statistical perspective on data mining – similarity measur ...
... Basic data mining tasks – data mining versus knowledge discovery in databases – data mining issues – data mining metrics – social implications of data mining – data mining from a database perspective. Data mining techniques: Introduction – a statistical perspective on data mining – similarity measur ...
slides - Charu Aggarwal
... DYCOS: DYnamic Classification algorithm with cOntent and Structure • Semi-bipartite content-structure transformation • Classification using a series of text and linkbased random walks • Accuracy analysis Experiments • NetKit-SRL Conclusion ...
... DYCOS: DYnamic Classification algorithm with cOntent and Structure • Semi-bipartite content-structure transformation • Classification using a series of text and linkbased random walks • Accuracy analysis Experiments • NetKit-SRL Conclusion ...
Hierarchical Clustering
... Map the clustering problem to a different domain and solve a related problem in that domain – Proximity matrix defines a weighted graph, where the nodes are the points being clustered, and the weighted edges represent the proximities between points – Clustering is equivalent to breaking the graph in ...
... Map the clustering problem to a different domain and solve a related problem in that domain – Proximity matrix defines a weighted graph, where the nodes are the points being clustered, and the weighted edges represent the proximities between points – Clustering is equivalent to breaking the graph in ...
DNA Microarrays K-means, a Clustering Technique
... Cluster data using Euclidean distance (or other distance metric) Calculate new center points for each cluster, using only points within the cluster Re-Cluster all data using the new center points (this step could cause some data points to be placed in a different cluster) Repeat steps 3 & 4 until no ...
... Cluster data using Euclidean distance (or other distance metric) Calculate new center points for each cluster, using only points within the cluster Re-Cluster all data using the new center points (this step could cause some data points to be placed in a different cluster) Repeat steps 3 & 4 until no ...
Data Mining A Tutorial
... • A clustering algorithm requires us to provide an initial best estimate about the total number of clusters in the data (supervised). • A clustering algorithm uses some method in an attempt to determine a best number of clusters (unsupervised) ...
... • A clustering algorithm requires us to provide an initial best estimate about the total number of clusters in the data (supervised). • A clustering algorithm uses some method in an attempt to determine a best number of clusters (unsupervised) ...
PPT
... Map the clustering problem to a different domain and solve a related problem in that domain – Proximity matrix defines a weighted graph, where the nodes are the points being clustered, and the weighted edges represent the proximities between points – Clustering is equivalent to breaking the graph in ...
... Map the clustering problem to a different domain and solve a related problem in that domain – Proximity matrix defines a weighted graph, where the nodes are the points being clustered, and the weighted edges represent the proximities between points – Clustering is equivalent to breaking the graph in ...
Comparison of Decision Tree and ANN Techniques for
... All these algorithms play a common role which helps to determine a model for the problem domain based on the data fed into the system. Data mining model can be created either predictive or descriptive in nature. A predictive model makes a prediction about the values of data using known results from ...
... All these algorithms play a common role which helps to determine a model for the problem domain based on the data fed into the system. Data mining model can be created either predictive or descriptive in nature. A predictive model makes a prediction about the values of data using known results from ...
A k-mean clustering algorithm for mixed numeric and categorical data
... process are (i) defining a similarity measure to judge the similarity (or distance) between different elements (ii) implementing an efficient algorithm to discover the clusters of most similar elements in an unsupervised way and (iii) derive a description that can characterize the elements of a cluster ...
... process are (i) defining a similarity measure to judge the similarity (or distance) between different elements (ii) implementing an efficient algorithm to discover the clusters of most similar elements in an unsupervised way and (iii) derive a description that can characterize the elements of a cluster ...
LSGI4241A
... activities, student presentation, and assignments. Lab practice includes lab and tutorial. Through these activities, students will be assessed about the fundamental knowledge in spatial data mining and the practical capabilities of performing spatial data mining using actual data sets. Problem based ...
... activities, student presentation, and assignments. Lab practice includes lab and tutorial. Through these activities, students will be assessed about the fundamental knowledge in spatial data mining and the practical capabilities of performing spatial data mining using actual data sets. Problem based ...
eneralized Partial Global Planning
... Merging spatial regions according to the spatial concept hierarchy. – Second step: Attribute-oriented induction. Non-spatial data at each merged regions are generalized at a given level by the threshold. ...
... Merging spatial regions according to the spatial concept hierarchy. – Second step: Attribute-oriented induction. Non-spatial data at each merged regions are generalized at a given level by the threshold. ...
A New Procedure of Clustering Based on Multivariate Outlier Detection
... (typically for the normal behavior) from the given data and then apply a statistical test to determine if an object belongs to this model or not. Objects that have low probability to belong to the statistical model are declared as outliers. However, distribution-based approaches cannot be applied in ...
... (typically for the normal behavior) from the given data and then apply a statistical test to determine if an object belongs to this model or not. Objects that have low probability to belong to the statistical model are declared as outliers. However, distribution-based approaches cannot be applied in ...
The Hong Kong Polytechnic University Subject Description
... activities, student presentation, and assignments. Lab practice includes lab and tutorial. Through these activities, students will be assessed about the fundamental knowledge in spatial data mining and the practical capabilities of performing spatial data mining using actual data sets. Problem based ...
... activities, student presentation, and assignments. Lab practice includes lab and tutorial. Through these activities, students will be assessed about the fundamental knowledge in spatial data mining and the practical capabilities of performing spatial data mining using actual data sets. Problem based ...
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.