Mining Moving Object Data for Discovery of Animal Movement Patterns
... (2) Relationship pattern. With a set of moving objects, one might want to know the relationships among the individuals. One of the most useful tasks is to find groups of objects that move together. By discovering such clusters, one can detect the communities of animals. There have been a lot of stud ...
... (2) Relationship pattern. With a set of moving objects, one might want to know the relationships among the individuals. One of the most useful tasks is to find groups of objects that move together. By discovering such clusters, one can detect the communities of animals. There have been a lot of stud ...
A Comparative Study of Data Mining Algorithms for Image
... through KNN, NN, MLPNN, and J48 methods to classify the lung image dataset. This system has been tested with lung images and it achieved satisfactory results in lung diseases image classification. The genetic algorithm is based on population search method its travels from one set of point to another ...
... through KNN, NN, MLPNN, and J48 methods to classify the lung image dataset. This system has been tested with lung images and it achieved satisfactory results in lung diseases image classification. The genetic algorithm is based on population search method its travels from one set of point to another ...
ENTROPY BASED TECHNIQUES WITH APPLICATIONS IN DATA
... The data set to be classified is divided into two parts, a training set and a testing set. A characteristic of classification is that there is a a well defined set of categories or classes. Classification is sometimes referred to as supervised learning. The machine algorithm to applied is trained us ...
... The data set to be classified is divided into two parts, a training set and a testing set. A characteristic of classification is that there is a a well defined set of categories or classes. Classification is sometimes referred to as supervised learning. The machine algorithm to applied is trained us ...
mike_phd_defense_final
... When one or both of the itemsets are not single items (attributes), it is not possible to directly apply most pairwise measures – Confidence is an exception ...
... When one or both of the itemsets are not single items (attributes), it is not possible to directly apply most pairwise measures – Confidence is an exception ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and transform it into a human-understandable structure.In data mining, association rule learning is a popular and well researc ...
... of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and transform it into a human-understandable structure.In data mining, association rule learning is a popular and well researc ...
Data Mining and Soft Computing
... distinct ti t subsets b t off customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. – Approach: • Collect different attributes of customers based on their geographical and lifestyle related information. • Find Fi d clusters l t off sim ...
... distinct ti t subsets b t off customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. – Approach: • Collect different attributes of customers based on their geographical and lifestyle related information. • Find Fi d clusters l t off sim ...
Temporal Sequence Classification in the Presence
... Over the last decade, the interest of discovering hidden information from sequences has increased. A sequence can be thought of as a list of events with a particular order. They appear in a broad range of real-world applications, from engineering to scientific research, finance and medicine [19] [4] ...
... Over the last decade, the interest of discovering hidden information from sequences has increased. A sequence can be thought of as a list of events with a particular order. They appear in a broad range of real-world applications, from engineering to scientific research, finance and medicine [19] [4] ...
APPLICATIONS OF DATA MINING IN E
... find information about some of the most important issues involved in real world application of DM technology. These issues include data preparation (e.g., cleaning and transformation), adaptation of existing methods to the specificities of an application, combination of different types of methods (e.g ...
... find information about some of the most important issues involved in real world application of DM technology. These issues include data preparation (e.g., cleaning and transformation), adaptation of existing methods to the specificities of an application, combination of different types of methods (e.g ...
To Study the Various Methods used in Data Mining
... generalized data mining tool that possess intelligence to select the data and data mining algorithms and up to some extent the knowledge discovery. 1.8 How Data Mining Works? How exactly is data mining able to tell you important things that you didn't know or what is going to happen next? The techni ...
... generalized data mining tool that possess intelligence to select the data and data mining algorithms and up to some extent the knowledge discovery. 1.8 How Data Mining Works? How exactly is data mining able to tell you important things that you didn't know or what is going to happen next? The techni ...
Frequency-aware Similarity Measures - Hasso-Plattner
... according to the name frequencies. We have two name attributes in our data model (FirstName and LastName) and need to handle several data quality problems: swapping of first and last name, typos, and combining two attributes (so that one frequency value is calculated). First and last name may be swi ...
... according to the name frequencies. We have two name attributes in our data model (FirstName and LastName) and need to handle several data quality problems: swapping of first and last name, typos, and combining two attributes (so that one frequency value is calculated). First and last name may be swi ...
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 ...
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.