From Data Mining to Knowledge Discovery in Databases
... It was used successfully on data from the Welfare Department of the State of Washington. In other areas, a well-publicized system is IBM’s ADVANCED SCOUT, a specialized data-mining system that helps National Basketball Association (NBA) coaches organize and interpret data from NBA games (U.S. News 1 ...
... It was used successfully on data from the Welfare Department of the State of Washington. In other areas, a well-publicized system is IBM’s ADVANCED SCOUT, a specialized data-mining system that helps National Basketball Association (NBA) coaches organize and interpret data from NBA games (U.S. News 1 ...
Future Reserch
... Fogarty, J., Ko, A.J., Aung, H.H., Golden, E., Tang, K.P. and Hudson, S.E. Examining task engagement in sensor-based statistical models of human interruptibility. In Proc. CHI 2005, ACM Press (2005), ...
... Fogarty, J., Ko, A.J., Aung, H.H., Golden, E., Tang, K.P. and Hudson, S.E. Examining task engagement in sensor-based statistical models of human interruptibility. In Proc. CHI 2005, ACM Press (2005), ...
Probabilistic Discovery of Time Series Motifs
... Definition 1. Time Series: A time series T = t1,…,tm is an ordered set of m real-valued variables. Time series can be very long, sometimes containing trillions of observations [12, 32]. We are typically not interested in any of the global properties of a time series; rather, we are interested in sub ...
... Definition 1. Time Series: A time series T = t1,…,tm is an ordered set of m real-valued variables. Time series can be very long, sometimes containing trillions of observations [12, 32]. We are typically not interested in any of the global properties of a time series; rather, we are interested in sub ...
Research on spatial data mining based on uncertainty in
... how to create a mining model of SDM. In fact, it is a kind of clustering algorithms. Clustering algorithm, which is also called aggregation algorithm, is an indirect data mining algorithms and does not use independent variables to get designated output. Different from classification model, clusteri ...
... how to create a mining model of SDM. In fact, it is a kind of clustering algorithms. Clustering algorithm, which is also called aggregation algorithm, is an indirect data mining algorithms and does not use independent variables to get designated output. Different from classification model, clusteri ...
Automatic Document Topic Identification Using Hierarchical
... topics, as well as for automatic grouping of related documents. This constitutes one of the main current challenges in text mining. We introduce in this thesis a novel approach for identifying document topics. In this approach, we try to utilize human background knowledge to help us to automatically ...
... topics, as well as for automatic grouping of related documents. This constitutes one of the main current challenges in text mining. We introduce in this thesis a novel approach for identifying document topics. In this approach, we try to utilize human background knowledge to help us to automatically ...
Open Challenges for Data Stream Mining Research
... Data streams present new challenges and opportunities with respect to protecting privacy and confidentiality in data mining. Privacy preserving data mining has been studied for over a decade (see. e.g. [3]). The main objective is to develop such data mining techniques that would not uncover informat ...
... Data streams present new challenges and opportunities with respect to protecting privacy and confidentiality in data mining. Privacy preserving data mining has been studied for over a decade (see. e.g. [3]). The main objective is to develop such data mining techniques that would not uncover informat ...
Efficient Data Mining Based on Formal Concept Analysis
... is above a given threshold minconf ∈ [0, 1]. Association rules are for instance used in warehouse basket analysis, where the warehouse management is interested in ...
... is above a given threshold minconf ∈ [0, 1]. Association rules are for instance used in warehouse basket analysis, where the warehouse management is interested in ...
03Preprocessing
... store cluster representation (e.g., centroid and diameter) only Can be very effective if data is clustered but not if data is “smeared” Can have hierarchical clustering and be stored in multidimensional index tree structures There are many choices of clustering definitions and clustering algorithms ...
... store cluster representation (e.g., centroid and diameter) only Can be very effective if data is clustered but not if data is “smeared” Can have hierarchical clustering and be stored in multidimensional index tree structures There are many choices of clustering definitions and clustering algorithms ...
A Study on Frequent Pattern Mining
... main steps in Apriori include candidate generation and testing, join step and prune step. This concept is very useful as it provides an understanding of how the search space of candidate patterns may be explored in order and nonredundant way. Apriori like methods[13,16] were developed by many number ...
... main steps in Apriori include candidate generation and testing, join step and prune step. This concept is very useful as it provides an understanding of how the search space of candidate patterns may be explored in order and nonredundant way. Apriori like methods[13,16] were developed by many number ...
Chapter 1 OUTLIER DETECTION
... In outward testing procedures, the sample of observations is first reduced to a smaller sample (e.g., by a factor of two), while the removed observations are kept in a reservoir. The statistics are calculated on the basis of the reduced sample and then the removed observations in the reservoir are t ...
... In outward testing procedures, the sample of observations is first reduced to a smaller sample (e.g., by a factor of two), while the removed observations are kept in a reservoir. The statistics are calculated on the basis of the reduced sample and then the removed observations in the reservoir are t ...
Ent SETS
... strongest of the wavelet coefficients • Similar to discrete Fourier transform (DFT), but better lossy compression, localized in space • Method: – Length, L, must be an integer power of 2 (padding with 0s, when necessary) – Each transform has 2 functions: smoothing, difference – Applies to pairs of d ...
... strongest of the wavelet coefficients • Similar to discrete Fourier transform (DFT), but better lossy compression, localized in space • Method: – Length, L, must be an integer power of 2 (padding with 0s, when necessary) – Each transform has 2 functions: smoothing, difference – Applies to pairs of d ...
Data Preprocessing
... store cluster representation (e.g., centroid and diameter) only Can be very effective if data is clustered but not if data is “smeared” Can have hierarchical clustering and be stored in multidimensional index tree structures There are many choices of clustering definitions and clustering algorithms ...
... store cluster representation (e.g., centroid and diameter) only Can be very effective if data is clustered but not if data is “smeared” Can have hierarchical clustering and be stored in multidimensional index tree structures There are many choices of clustering definitions and clustering algorithms ...
Symbolic data analysis of complex data
... What is the actual failure which has produced the SDA Paradigm? The failure is that in the actual practice Only the “individual” kind of observations is considered. Therefore these individual observations are only described by standard numerical and categorical variables. ...
... What is the actual failure which has produced the SDA Paradigm? The failure is that in the actual practice Only the “individual” kind of observations is considered. Therefore these individual observations are only described by standard numerical and categorical variables. ...
Knowledge Discovery for Semantic Web
... Knowledge discovery approaches can be used on multimodal data consisting of different data types including databases, text, images, video, graphs. The general idea is to preprocess the data and represent it in a way appropriate for further analysis with knowledge discovery approaches. For instance, ...
... Knowledge discovery approaches can be used on multimodal data consisting of different data types including databases, text, images, video, graphs. The general idea is to preprocess the data and represent it in a way appropriate for further analysis with knowledge discovery approaches. For instance, ...
Association Rule Mining using Apriori Algorithm: A Survey
... is mined and the knowledge obtained is interpreted. Optimization of association rule mining and apriori algorithm Using Ant colony optimization [3].This paper is on Apriori algorithm and association rule mining to improved algorithm based on the Ant colony optimization algorithm. ACO was introduced ...
... is mined and the knowledge obtained is interpreted. Optimization of association rule mining and apriori algorithm Using Ant colony optimization [3].This paper is on Apriori algorithm and association rule mining to improved algorithm based on the Ant colony optimization algorithm. ACO was introduced ...
Roiger_DM_ch03 - Gonzaga University
... • The chapter introduces several common data mining techniques. • In Section 3.1, it focus on supervised learning by presenting a standard algorithm for creating decision trees. • In Section 3.2, an efficient technique for generating association rules is presented. • In Section 3.3, unsupervised clu ...
... • The chapter introduces several common data mining techniques. • In Section 3.1, it focus on supervised learning by presenting a standard algorithm for creating decision trees. • In Section 3.2, an efficient technique for generating association rules is presented. • In Section 3.3, unsupervised clu ...
Classification Based On Association Rule Mining Technique
... anyone practices through his life. One can classify human beings based on their race or can categorize products in a supermarket based on the consumers shopping choices. In general, classification involves examining the features of new objects and trying to assign it to one of the predefined set of ...
... anyone practices through his life. One can classify human beings based on their race or can categorize products in a supermarket based on the consumers shopping choices. In general, classification involves examining the features of new objects and trying to assign it to one of the predefined set of ...
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.