C2P: Clustering based on Closest Pairs
... Dmin is used by the single-link hierarchical clustering algorithm, and joins the two clusters which contain the closest pair of points. It can detect elongated or concentric clusters, but it is sensitive to the “chainingeffect”. Each cluster is represented by all its points. Dmean is appropriate for ...
... Dmin is used by the single-link hierarchical clustering algorithm, and joins the two clusters which contain the closest pair of points. It can detect elongated or concentric clusters, but it is sensitive to the “chainingeffect”. Each cluster is represented by all its points. Dmean is appropriate for ...
fulltext - Simple search
... called a cluster. It consists of objects that embody some similarities and are dissimilar to objects of other groups (Berkhin, 2002). We can find many definitions for clustering in the literatures (Jain et al., 1999; Xu & Wunsch, 2005; Gower, 1971; Jain & Dubes, 1988; Mocian, 2009; Tan et al., 2005) ...
... called a cluster. It consists of objects that embody some similarities and are dissimilar to objects of other groups (Berkhin, 2002). We can find many definitions for clustering in the literatures (Jain et al., 1999; Xu & Wunsch, 2005; Gower, 1971; Jain & Dubes, 1988; Mocian, 2009; Tan et al., 2005) ...
View PDF - International Journal of Computer Science and Mobile
... Ant colony optimization Ant colony optimization or ACO is a class of optimization algorithms modeled on the actions of an ant colony. Artificial 'ants' - simulation agents - locate optimal solutions by moving through a parameter space representing all possible solutions. Real ants lay down pheromone ...
... Ant colony optimization Ant colony optimization or ACO is a class of optimization algorithms modeled on the actions of an ant colony. Artificial 'ants' - simulation agents - locate optimal solutions by moving through a parameter space representing all possible solutions. Real ants lay down pheromone ...
as a PDF
... - Ideally would like to select only the useful quadratic terms - Can generalize this idea to higher-order interactions ...
... - Ideally would like to select only the useful quadratic terms - Can generalize this idea to higher-order interactions ...
DP33701704
... clustering algorithm depends upon the type of data. Clustering can be applied to numerical data and categorical data. The numerical data consist of numeric attributes, such as age, cost such attribute values can be ordered in specific manner and the properties can be used to apply the distance measu ...
... clustering algorithm depends upon the type of data. Clustering can be applied to numerical data and categorical data. The numerical data consist of numeric attributes, such as age, cost such attribute values can be ordered in specific manner and the properties can be used to apply the distance measu ...
Space-Partitioning-Based Bulk-Loading for the NSP-Tree in Non-ordered Discrete Data Spaces," DEXA 2008, September 1-5, Turin, Italy, Gang Quian, Hyun-Jeon Seik,Qiang Zhu,Sakti Pramanik.
... efficient similarity queries in NDDSs, the space-partitioning-based NSP-tree was proposed in [14]. The conventional TL algorithm of the NSP-tree inserts one vector at time into a leaf node of the tree. When a leaf overflows, its corresponding data space is split into two subspaces and its vectors are m ...
... efficient similarity queries in NDDSs, the space-partitioning-based NSP-tree was proposed in [14]. The conventional TL algorithm of the NSP-tree inserts one vector at time into a leaf node of the tree. When a leaf overflows, its corresponding data space is split into two subspaces and its vectors are m ...
Application of Data Mining Techniques for Improving Software
... text mining: start list, stop list, and synonym list. A stop list contains terms such as articles, prepositions, and conjunctions that are not relevant in text mining. If a term in the document is found in the stop list it is not entered into matrix. Terms in the start list, on the other hand, are h ...
... text mining: start list, stop list, and synonym list. A stop list contains terms such as articles, prepositions, and conjunctions that are not relevant in text mining. If a term in the document is found in the stop list it is not entered into matrix. Terms in the start list, on the other hand, are h ...
The Use of Heuristics in Decision Tree Learning Optimization
... Simulated Annealing has been used in combination with Tabu Search and Genetic Algorithms to build decision trees. N.Mishra et al. [17] propose a hybrid algorithm named tabusimulated annealing to solve complex problems of the theory of constraints (TOC), an effective management philosophy for solving ...
... Simulated Annealing has been used in combination with Tabu Search and Genetic Algorithms to build decision trees. N.Mishra et al. [17] propose a hybrid algorithm named tabusimulated annealing to solve complex problems of the theory of constraints (TOC), an effective management philosophy for solving ...
Clustering
... • The number of samples to be processed is very high. Clustering in general is NP-hard, and practical and successful data mining algorithms usually scale linear or log-linear. Quadratic and cubic scaling may also be allowable but a linear behavior is highly desirable. ...
... • The number of samples to be processed is very high. Clustering in general is NP-hard, and practical and successful data mining algorithms usually scale linear or log-linear. Quadratic and cubic scaling may also be allowable but a linear behavior is highly desirable. ...
Rise of Data Mining: Current and Future Application Areas
... various aspects of human life, may it be in the form of modernization of banking, land records, libraries, or data regarding population. This advent in various fields of human life has led to the very large volumes of data stored in various formats like documents, records, images, sound recordings, ...
... various aspects of human life, may it be in the form of modernization of banking, land records, libraries, or data regarding population. This advent in various fields of human life has led to the very large volumes of data stored in various formats like documents, records, images, sound recordings, ...
Cluster Analysis of Economic Data
... Cluster analysis is a strong tool of the multivariate exploratory data analysis. It involves a great amount of techniques, methods and algorithms which can be applied in various fields, including economy. However, in most of research papers containing cluster analysis of economic data the classical ...
... Cluster analysis is a strong tool of the multivariate exploratory data analysis. It involves a great amount of techniques, methods and algorithms which can be applied in various fields, including economy. However, in most of research papers containing cluster analysis of economic data the classical ...
Data Cleaning: A Framework for Robust Data Quality In
... will be applied on the cleaned data. Data received at the data warehouse from external sources usually contains errors, e.g. Spelling mistakes, inconsistent conventions across data sources, and/or Missing fields, Contradicting data, Cryptic data, Noisy values, Data Integration problems, Reused prima ...
... will be applied on the cleaned data. Data received at the data warehouse from external sources usually contains errors, e.g. Spelling mistakes, inconsistent conventions across data sources, and/or Missing fields, Contradicting data, Cryptic data, Noisy values, Data Integration problems, Reused prima ...
A Framework of Business Intelligence
... D. Method-driven and Knowledge-driven Data Mining Method-driven DM is a common method on data mining nowadays. It works on developing and applying algorithms to model and mine data. Until now, there have been a large n of studies established on the basics of mining algorithms, both for centralized a ...
... D. Method-driven and Knowledge-driven Data Mining Method-driven DM is a common method on data mining nowadays. It works on developing and applying algorithms to model and mine data. Until now, there have been a large n of studies established on the basics of mining algorithms, both for centralized a ...
IEEE Paper Template in A4 (V1) - International Journal of Computer
... Apriori algorithm was proposed by R. Agrawal et al in [28] which is used to obtain frequent itemsets from the database. MINimal Infrequent Itemsets (MINIT) is the first algorithm designed specifically for mining minimal infrequent itemsets [2]. MINIT computes both minimal and non-minimal (unweighted ...
... Apriori algorithm was proposed by R. Agrawal et al in [28] which is used to obtain frequent itemsets from the database. MINimal Infrequent Itemsets (MINIT) is the first algorithm designed specifically for mining minimal infrequent itemsets [2]. MINIT computes both minimal and non-minimal (unweighted ...
Chapter 2 Literature Review 2.1 Data Mining
... strengths of the relationship using some numerical scale. Market basket analysis is a well known association application; it can be performed on the retail data of customer transactions to find out what items are frequently purchased together (A.K.A. itemsets). Apriori is the basic algorithm for fin ...
... strengths of the relationship using some numerical scale. Market basket analysis is a well known association application; it can be performed on the retail data of customer transactions to find out what items are frequently purchased together (A.K.A. itemsets). Apriori is the basic algorithm for fin ...
a review of data mining system and its appication
... The life cycle of a data mining project consists of six phases. The sequence of the phases is not rigid. Moving back and forth between different phases is always required. It depends on the outcome of each phase. The main phases are: 1. Business Understanding: This phase focuses on understanding the ...
... The life cycle of a data mining project consists of six phases. The sequence of the phases is not rigid. Moving back and forth between different phases is always required. It depends on the outcome of each phase. The main phases are: 1. Business Understanding: This phase focuses on understanding the ...
DATAMINING AND ISLAMIC KNOWLEDGE EXCTRACTION
... knowledge resource, and proposes approach to classify Hadith to its ...
... knowledge resource, and proposes approach to classify Hadith to its ...
A Survey on Algorithms for Market Basket Analysis
... CBA selects high confidence rules to represent the classifier. Finally, to predict a test case, CBA applies the highest confidence rule whose body matches the test case. Experimental results designated that CBA derives higher quality classifiers with regards to accuracy than rule induction and decis ...
... CBA selects high confidence rules to represent the classifier. Finally, to predict a test case, CBA applies the highest confidence rule whose body matches the test case. Experimental results designated that CBA derives higher quality classifiers with regards to accuracy than rule induction and decis ...
Security of Cloud from Data Mining based Attacks Inderjit Kaur
... easily be declared better than the analyses of past events done by decision support systems. Traditional methods are not very effective now as the size and complexity of datasets has increased. Automatic data processing which can also be indirect has been added in data analysis. Some other invention ...
... easily be declared better than the analyses of past events done by decision support systems. Traditional methods are not very effective now as the size and complexity of datasets has increased. Automatic data processing which can also be indirect has been added in data analysis. Some other invention ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.