Data Mining for Business Intelligence - MCST-CS
... Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
... Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
Representing, Storing and Mining Moving Objects Data
... Another feature of moving objects is that they can implement monitoring applications for modeling and querying moving objects, using methodologies and techniques such as the implementation of data model within algebra modules that offers a set of type constructors and a set of operators, developed b ...
... Another feature of moving objects is that they can implement monitoring applications for modeling and querying moving objects, using methodologies and techniques such as the implementation of data model within algebra modules that offers a set of type constructors and a set of operators, developed b ...
Knowledge Discovery and Data Mining - OPUS at UTS
... pattern recognition. These methods do not tend to contain (or bring to the problem) specific domain specific information. In this way, they may be termed “knowledge-empty.” However, in some real-world areas, it is important to enrich the data with external background knowledge so as to provide conte ...
... pattern recognition. These methods do not tend to contain (or bring to the problem) specific domain specific information. In this way, they may be termed “knowledge-empty.” However, in some real-world areas, it is important to enrich the data with external background knowledge so as to provide conte ...
Data Visualization
... Keep only best substructures on queue (specified by beam width) 4. Terminate when queue is empty or #discovered substructures >= limit 5. Compress graph and repeat to generate hierarchical description Note: polynomially constrained [IEEE Exp96] ...
... Keep only best substructures on queue (specified by beam width) 4. Terminate when queue is empty or #discovered substructures >= limit 5. Compress graph and repeat to generate hierarchical description Note: polynomially constrained [IEEE Exp96] ...
Turban: Chapter 5: Data Mining for Business Intelligence
... Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
... Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
Operations Research & Data Mining
... dates back to the 1960s, most problems are still open or need more research Need to be aware of the key concerns of data mining: extracting meaningful, previously unknown patterns or knowledge from large ...
... dates back to the 1960s, most problems are still open or need more research Need to be aware of the key concerns of data mining: extracting meaningful, previously unknown patterns or knowledge from large ...
Introduction to Knowledge Discovery and Data Mining
... Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases Data mining: a misnomer? Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, info ...
... Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases Data mining: a misnomer? Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, info ...
Data Mining for Modeling Chiller Systems in Data Centers
... into the economizer. Flashed gases enter the compressor while the liquid flows into the evaporator to complete the circuit. Some terms used in the context of chillers are given below. ...
... into the economizer. Flashed gases enter the compressor while the liquid flows into the evaporator to complete the circuit. Some terms used in the context of chillers are given below. ...
OASIS paper v25 LNCS
... Table 1 presents the attributes (factors) in the analyzed dataset, explaining their meanings and providing their respective value ranges. Figure 1 presents the data mining process followed in the study. Figure 2 presents the roles assigned to attributes in the process (“Select Attributes” block of ...
... Table 1 presents the attributes (factors) in the analyzed dataset, explaining their meanings and providing their respective value ranges. Figure 1 presents the data mining process followed in the study. Figure 2 presents the roles assigned to attributes in the process (“Select Attributes” block of ...
Data Mining as a Tool for Environmental Scientists
... to do this, an efficient tree is built using the variables YearModulo4 and YearModulo100. Where extra features are constructed in this way, expert knowledge is usually the guide. Exploratory variable creation without such assistance is almost always prohibitively time consuming, and as noted in Sect ...
... to do this, an efficient tree is built using the variables YearModulo4 and YearModulo100. Where extra features are constructed in this way, expert knowledge is usually the guide. Exploratory variable creation without such assistance is almost always prohibitively time consuming, and as noted in Sect ...
A Hash based Mining Algorithm for Maximal Frequent Item Sets
... also. In general, frequent patterns like tree structures, graphs can be generated using the same principle. There are many applications where the frequent itemset mining is applicable. In short, they can be listed as market-basket analysis, bioinformatics, networks and most in many analyses. Agarwal ...
... also. In general, frequent patterns like tree structures, graphs can be generated using the same principle. There are many applications where the frequent itemset mining is applicable. In short, they can be listed as market-basket analysis, bioinformatics, networks and most in many analyses. Agarwal ...
DM3: Input: Concepts, instances, attributes
... Reason: data has not been collected for mining it Result: errors and omissions that don’t affect original purpose of data (e.g. age of customer) Typographical errors in nominal attributes values need to be checked for consistency Typographical and measurement errors in numeric attributes ...
... Reason: data has not been collected for mining it Result: errors and omissions that don’t affect original purpose of data (e.g. age of customer) Typographical errors in nominal attributes values need to be checked for consistency Typographical and measurement errors in numeric attributes ...
DM3: Input: Concepts, instances, attributes
... Reason: data has not been collected for mining it Result: errors and omissions that don’t affect original purpose of data (e.g. age of customer) Typographical errors in nominal attributes values need to be checked for consistency Typographical and measurement errors in numeric attributes ...
... Reason: data has not been collected for mining it Result: errors and omissions that don’t affect original purpose of data (e.g. age of customer) Typographical errors in nominal attributes values need to be checked for consistency Typographical and measurement errors in numeric attributes ...
IJARCCE 20
... individual, in other words how close the candidate solution is from being optimal. Based on the fitness value, individuals are selected to mate. This process creates a new individual by combining two or more chromosomes, this process is called crossover. They are combined with each other in the hope ...
... individual, in other words how close the candidate solution is from being optimal. Based on the fitness value, individuals are selected to mate. This process creates a new individual by combining two or more chromosomes, this process is called crossover. They are combined with each other in the hope ...
Finding Motifs in Time Series
... interesting problem is the enumeration of previously unknown, frequently occurring patterns. We call such patterns “motifs,” because of their close analogy to their discrete counterparts in computation biology. An efficient motif discovery algorithm for time series would be useful as a tool for summ ...
... interesting problem is the enumeration of previously unknown, frequently occurring patterns. We call such patterns “motifs,” because of their close analogy to their discrete counterparts in computation biology. An efficient motif discovery algorithm for time series would be useful as a tool for summ ...
Data Mining: Concepts and Techniques — Slides for Textbook
... Clustering Definition • Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that – Data points in one cluster are more similar to one ...
... Clustering Definition • Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that – Data points in one cluster are more similar to one ...
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.