Mining Subspace Clusters: Enhanced Models, Efficient Algorithms
... attributes “average traveling frequency” and “income” with other “globetrotters” having high values in both of these attributes. The same customer might be a “healthy oldie” which could be specified by a high “age” and low “blood pressure” (cf. Fig. 1). We observe for each customer multiple possible ...
... attributes “average traveling frequency” and “income” with other “globetrotters” having high values in both of these attributes. The same customer might be a “healthy oldie” which could be specified by a high “age” and low “blood pressure” (cf. Fig. 1). We observe for each customer multiple possible ...
Characterization of unsupervised clusters with the simplest
... examples into clusters such that examples within a cluster are similar. Recently, an important research effort has been devoted to the integration of cluster characterization into such methods. In conceptual clustering, examples are given by attribute-value pairs (e.g., the definition of medical sym ...
... examples into clusters such that examples within a cluster are similar. Recently, an important research effort has been devoted to the integration of cluster characterization into such methods. In conceptual clustering, examples are given by attribute-value pairs (e.g., the definition of medical sym ...
Slides
... 1,2 is around 1: equivalence of numerical results obtained between the two models M1 and M2 for the calculation of X. 1,2 is either very small or very great compared to 1: the numerical results between M1 and M2 are significantly different. ...
... 1,2 is around 1: equivalence of numerical results obtained between the two models M1 and M2 for the calculation of X. 1,2 is either very small or very great compared to 1: the numerical results between M1 and M2 are significantly different. ...
S4904131136
... [20]. It is merely used to segment data into some homogeneous groups so that a quality model can be built on each group. Another clustering limitation is the ability to evaluate and compare their performance. The reason for this is the lack of an objective evaluation criterion that is independent of ...
... [20]. It is merely used to segment data into some homogeneous groups so that a quality model can be built on each group. Another clustering limitation is the ability to evaluate and compare their performance. The reason for this is the lack of an objective evaluation criterion that is independent of ...
CS 432-CS 536-Introduction to Data Mining-Data
... Data mining or discovery of knowledge in large datasets has created a lot of interest in the business and research communities in recent years. The tremendous increase in the generation and collection of data has highlighted the need for systems that can extract useful and actionable knowledge from ...
... Data mining or discovery of knowledge in large datasets has created a lot of interest in the business and research communities in recent years. The tremendous increase in the generation and collection of data has highlighted the need for systems that can extract useful and actionable knowledge from ...
Intrusion Detection Technique by using K
... one of the looms to resolve the problem of network security. Imperfectness of intrusion detection systems (IDS) has given an opportunity for data mining to make several important contributions to the field of intrusion detection. In recent years, many researchers are using data mining techniques for ...
... one of the looms to resolve the problem of network security. Imperfectness of intrusion detection systems (IDS) has given an opportunity for data mining to make several important contributions to the field of intrusion detection. In recent years, many researchers are using data mining techniques for ...
M.TECH. DEGREE EXAMINATION, December 2013 Branch
... c. Define the terms sensitivity, specificity, accuracy, precision and write their equations. (5 marks) 7. a. What is cluster analysis? What are its desired features? ...
... c. Define the terms sensitivity, specificity, accuracy, precision and write their equations. (5 marks) 7. a. What is cluster analysis? What are its desired features? ...
CIS732-Lecture-36
... – Generate “fictional data points”, weighted according to this probability • P(Coin = 1 | x) = P(x | Coin = 1) P(Coin = 1) / P(x) based on our guess of , p, q • Expectation step (the “E” in EM) – Now, can find most likely values of parameters , p, q given “fictional” data • Use gradient descent to ...
... – Generate “fictional data points”, weighted according to this probability • P(Coin = 1 | x) = P(x | Coin = 1) P(Coin = 1) / P(x) based on our guess of , p, q • Expectation step (the “E” in EM) – Now, can find most likely values of parameters , p, q given “fictional” data • Use gradient descent to ...
marked - Kansas State University
... – Generate “fictional data points”, weighted according to this probability • P(Coin = 1 | x) = P(x | Coin = 1) P(Coin = 1) / P(x) based on our guess of , p, q • Expectation step (the “E” in EM) – Now, can find most likely values of parameters , p, q given “fictional” data • Use gradient descent to ...
... – Generate “fictional data points”, weighted according to this probability • P(Coin = 1 | x) = P(x | Coin = 1) P(Coin = 1) / P(x) based on our guess of , p, q • Expectation step (the “E” in EM) – Now, can find most likely values of parameters , p, q given “fictional” data • Use gradient descent to ...
Document
... • Statistical analysis packages (e.g., : S++, SPSS) can be interfaced with databases (further ignored) • Data mining seeks to discover knowledge automatically in the form of statistical rules and patterns from large databases. • A data warehouse archives information gathered from multiple sources, a ...
... • Statistical analysis packages (e.g., : S++, SPSS) can be interfaced with databases (further ignored) • Data mining seeks to discover knowledge automatically in the form of statistical rules and patterns from large databases. • A data warehouse archives information gathered from multiple sources, a ...
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.