A Framework for On-Demand Classification of Evolving
... We note that the determination of the appropriate window of data depends upon the level of evolution of the data. This level of evolution is not known a priori and is difficult to determine efficiently for a fast stream. The high volume of the data stream makes it essential to store summary statisti ...
... We note that the determination of the appropriate window of data depends upon the level of evolution of the data. This level of evolution is not known a priori and is difficult to determine efficiently for a fast stream. The high volume of the data stream makes it essential to store summary statisti ...
Scalable Techniques for Mining Causal ...
... expensive, it is also difficult to interpret. We believe isolated causal relationships, involving only pairs or small sets of items, are easier to interpret. For many market basket problems, discovering that two items are not causally related, or at least not directly causally related (that is, one ...
... expensive, it is also difficult to interpret. We believe isolated causal relationships, involving only pairs or small sets of items, are easier to interpret. For many market basket problems, discovering that two items are not causally related, or at least not directly causally related (that is, one ...
PPT
... – using a sample will work almost as well as using the entire data sets, if the sample is representative – A sample is representative if it has approximately the same property (of interest) as the original set of data ...
... – using a sample will work almost as well as using the entire data sets, if the sample is representative – A sample is representative if it has approximately the same property (of interest) as the original set of data ...
Data Mining Unit 1 - cse505fall2014
... • How to get the job? An affinity for numbers is key, as well as a command of computing, statistics, math and analytics. One can't underestimate the importance of soft skills either. Data scientists work closely with management and need to express themselves clearly. • What makes it great? This is a ...
... • How to get the job? An affinity for numbers is key, as well as a command of computing, statistics, math and analytics. One can't underestimate the importance of soft skills either. Data scientists work closely with management and need to express themselves clearly. • What makes it great? This is a ...
Multimedia data mining: state of the art and challenges
... Data mining techniques on audio, video, text or image data are generally used to achieve two kinds of tasks (1) Descriptive Mining characterizes the general properties of the data in the database, and (2) Predictive Mining performs inference on the current data in order to make predictions. The foll ...
... Data mining techniques on audio, video, text or image data are generally used to achieve two kinds of tasks (1) Descriptive Mining characterizes the general properties of the data in the database, and (2) Predictive Mining performs inference on the current data in order to make predictions. The foll ...
Multivariate Maximal Correlation Analysis
... detect complex interactions in high dimensional data. For example, genes may reveal only a weak correlation with a disease if each gene is considered individually, while when considered as a group of genes the correlation may be very strong (Zhang et al., 2008). In such applications pairwise correla ...
... detect complex interactions in high dimensional data. For example, genes may reveal only a weak correlation with a disease if each gene is considered individually, while when considered as a group of genes the correlation may be very strong (Zhang et al., 2008). In such applications pairwise correla ...
Introduction to Spatial Data Mining
... Association rule given item-types and transactions assumes spatial data can be decomposed into transactions However, such decomposition may alter spatial patterns ...
... Association rule given item-types and transactions assumes spatial data can be decomposed into transactions However, such decomposition may alter spatial patterns ...
Real Time Data Mining-based Intrusion Detection
... Security of network systems is becoming increasingly important as more and more sensitive information is being stored and manipulated online. Intrusion Detection Systems (IDSs) have thus become a critical technology to help protect these systems. Most IDSs are based on hand-crafted signatures that a ...
... Security of network systems is becoming increasingly important as more and more sensitive information is being stored and manipulated online. Intrusion Detection Systems (IDSs) have thus become a critical technology to help protect these systems. Most IDSs are based on hand-crafted signatures that a ...
Outlier Detection Methods
... • Assume the normal objects are somewhat ``clustered'‘ into multiple groups, each having some distinct features • An outlier is expected to be far away from any groups of normal objects • Weakness: Cannot detect collective outlier effectively ‐ Normal objects may not share any strong patterns, but ...
... • Assume the normal objects are somewhat ``clustered'‘ into multiple groups, each having some distinct features • An outlier is expected to be far away from any groups of normal objects • Weakness: Cannot detect collective outlier effectively ‐ Normal objects may not share any strong patterns, but ...
The PDF of the Chapter - A Programmer`s Guide to Data Mining
... Let’s say we are doing 10-fold cross validation. We start at the beginning of the list and put every ten people in a different bucket. In this case we have 10 basketball players in both the first and second buckets. The third bucket has both basketball players and gymnasts. The fourth and fifth buc ...
... Let’s say we are doing 10-fold cross validation. We start at the beginning of the list and put every ten people in a different bucket. In this case we have 10 basketball players in both the first and second buckets. The third bucket has both basketball players and gymnasts. The fourth and fifth buc ...
The application of data mining methods
... has now become a global facility that almost covers every hole and corner on this planet. As a main part of the Internet, network protocols have been well developed to meet a wide range of practical applications. However, with the continuous expansion of its scale to both services and users, the pro ...
... has now become a global facility that almost covers every hole and corner on this planet. As a main part of the Internet, network protocols have been well developed to meet a wide range of practical applications. However, with the continuous expansion of its scale to both services and users, the pro ...
spatial data mining techniques
... Neighborhood graphs will in general contain many paths which are irrelevant if not “mislead ing” for spatial data mining algorithms. For finding significant spatial patterns, we have to consider only certain classes of paths which are “leading away” from the starting object in some straightforward s ...
... Neighborhood graphs will in general contain many paths which are irrelevant if not “mislead ing” for spatial data mining algorithms. For finding significant spatial patterns, we have to consider only certain classes of paths which are “leading away” from the starting object in some straightforward s ...
Document
... imprecise concepts like “slightly”, “quite”, “very” are definable using fuzzy logic. It allows partial. Nearest-Neighbour: Nearest-neighbour learners (Cover and Hart 1967) are very different from any of the learning methods just described in that no explicit model is ever built. That is, there is no ...
... imprecise concepts like “slightly”, “quite”, “very” are definable using fuzzy logic. It allows partial. Nearest-Neighbour: Nearest-neighbour learners (Cover and Hart 1967) are very different from any of the learning methods just described in that no explicit model is ever built. That is, there is no ...
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.