Fuzzy based clustering algorithm for privacy preserving data mining
... be reduced (Domingo-Ferrer and Mateo-Sanz, 2001). Micro-aggregation is another technique for data masking (Defays and Anwar, 1998; Domingo-Ferrer and Mateo-Sanz, 2002). It aggregates the record values of attributes that is intended to reduce re-identification risk. In single ranking micro-aggregatio ...
... be reduced (Domingo-Ferrer and Mateo-Sanz, 2001). Micro-aggregation is another technique for data masking (Defays and Anwar, 1998; Domingo-Ferrer and Mateo-Sanz, 2002). It aggregates the record values of attributes that is intended to reduce re-identification risk. In single ranking micro-aggregatio ...
Intrusion Detection System by using K
... performance is in terms of execution speed, and the second reason is scalability. SVMs are relatively insensitive to the number of data points, and the classification complexity does not depend on the dimensionality of the feature space [11]. K -Means Clustering The K-means algorithm, starting with ...
... performance is in terms of execution speed, and the second reason is scalability. SVMs are relatively insensitive to the number of data points, and the classification complexity does not depend on the dimensionality of the feature space [11]. K -Means Clustering The K-means algorithm, starting with ...
Spatio-Temporal Outlier Detection in Precipitation Data
... The Exact-Grid Top-k algorithm finds the top-k outliers for each time period by keeping track of the highest discrepancy regions as they are found. As it iterates through all the region shapes, it may find a new region that has a discrepancy value higher than the lowest discrepancy value (kth value) ...
... The Exact-Grid Top-k algorithm finds the top-k outliers for each time period by keeping track of the highest discrepancy regions as they are found. As it iterates through all the region shapes, it may find a new region that has a discrepancy value higher than the lowest discrepancy value (kth value) ...
Derive high confidence rules for spatial data using count cube
... number of items. Partitioning will also produce more interesting and general rules by using intervals instead of single values in the rules. There are several ways to partition the data, such as equi-length, equi-depth, and user-defined partitioning. Equi-length partitioning is a simple but useful m ...
... number of items. Partitioning will also produce more interesting and general rules by using intervals instead of single values in the rules. There are several ways to partition the data, such as equi-length, equi-depth, and user-defined partitioning. Equi-length partitioning is a simple but useful m ...
ARAA: A Fast Advanced Reverse Apriori Algorithm for Mining
... transaction that contains the largest itemsets is taken that forms the C1 table. The candidate itemsets and the frequent itemsets are generated together in the proposed algorithm. The table contains the information related to the support or counts as well the transaction which contains that itemsets ...
... transaction that contains the largest itemsets is taken that forms the C1 table. The candidate itemsets and the frequent itemsets are generated together in the proposed algorithm. The table contains the information related to the support or counts as well the transaction which contains that itemsets ...
YADING: Fast Clustering of Large-Scale Time Series Data
... Partitioning methods identify k partitions of the input data with each partition representing a cluster. Partitioning methods need manual specification of k as the number of clusters. k-means and kmedoid are typical partitioning algorithms. CLARANS [19] is an improved k-medoid method, and it is more ...
... Partitioning methods identify k partitions of the input data with each partition representing a cluster. Partitioning methods need manual specification of k as the number of clusters. k-means and kmedoid are typical partitioning algorithms. CLARANS [19] is an improved k-medoid method, and it is more ...
Analysis of Twitter Data Using a Multiple
... Recently, social networks and online communities, such as Twitter and Facebook, have become a powerful source of knowledge being daily accessed by millions of people. A particular attention has been paid to the analysis of the User Generated Content (UGC) coming from Twitter, which is one of the mo ...
... Recently, social networks and online communities, such as Twitter and Facebook, have become a powerful source of knowledge being daily accessed by millions of people. A particular attention has been paid to the analysis of the User Generated Content (UGC) coming from Twitter, which is one of the mo ...
density based subspace clustering
... information retrieval, machine learning, but significant issues still remain (Steinbach et al., 2003). This tools to divide data into meaningful or useful clusters; most of the common algorithms fail to generate meaningful results because of the inherent of the objects. High dimensional data, spread ...
... information retrieval, machine learning, but significant issues still remain (Steinbach et al., 2003). This tools to divide data into meaningful or useful clusters; most of the common algorithms fail to generate meaningful results because of the inherent of the objects. High dimensional data, spread ...
Introducing Economic Order Quantity Model for Inventory Control in
... customer transactions to improve sales, determining product shelving and supplier selection. For this purpose, Economic Order Quantity model is applied on the forecasted demands using simple moving average, linear regression, back propagation algorithm and afterwards a comparative analysis is conduc ...
... customer transactions to improve sales, determining product shelving and supplier selection. For this purpose, Economic Order Quantity model is applied on the forecasted demands using simple moving average, linear regression, back propagation algorithm and afterwards a comparative analysis is conduc ...
Discovery of Scalable Association Rules from Large Set of
... yet elective distance function to make it efficient. Most of the times, quality of clusters depreciates as we try to improve speed of clustering algorithm [11] . K-means is the most intuitive and popular clustering algorithm. However, the classical K-means suffers from several flaws. First, the algo ...
... yet elective distance function to make it efficient. Most of the times, quality of clusters depreciates as we try to improve speed of clustering algorithm [11] . K-means is the most intuitive and popular clustering algorithm. However, the classical K-means suffers from several flaws. First, the algo ...
OPTICS-OF: Identifying Local Outliers
... this, we do not explicitly label the objects as “outlier” or “not outlier”; instead we compute the level of outlier-ness for every object by assigning an outlier factor. Definition 3: (ε-neighborhood and k-distance of an object p) Let p be an object from a database D, let ε be a distance value, let ...
... this, we do not explicitly label the objects as “outlier” or “not outlier”; instead we compute the level of outlier-ness for every object by assigning an outlier factor. Definition 3: (ε-neighborhood and k-distance of an object p) Let p be an object from a database D, let ε be a distance value, let ...
Feature Selection Algorithm with Discretization and PSO
... combinatorial optimization problems to continuous optimization problems, single and multi-objective problems, etc. In this study, we propose a new discretization method that is applied for continuous attributes to convert the discrete values after applied feature subset selection based on PSO techni ...
... combinatorial optimization problems to continuous optimization problems, single and multi-objective problems, etc. In this study, we propose a new discretization method that is applied for continuous attributes to convert the discrete values after applied feature subset selection based on PSO techni ...
Clustering
... Clustering: general problem description Given: A data set with N d-dimensional data items. ...
... Clustering: general problem description Given: A data set with N d-dimensional data items. ...
Collaborative Document Clustering
... can be augmented or enhanced by having access to summarized cluster information from peer nodes. To better motivate the above scenarios, consider a set of digital libraries (e.g. archived articles from online publishers). Each digital library can form an opinion about the topic groups found in its c ...
... can be augmented or enhanced by having access to summarized cluster information from peer nodes. To better motivate the above scenarios, consider a set of digital libraries (e.g. archived articles from online publishers). Each digital library can form an opinion about the topic groups found in its c ...
Classification using Association Rule Mining
... how to efficiently find out the high quality rules using association rule mining and how to generate more accurate classifier, (2) scalability: it is important when there exist large training data sets, huge number of rules and long pattern rules. The efficiency and accuracy typically affect each ot ...
... how to efficiently find out the high quality rules using association rule mining and how to generate more accurate classifier, (2) scalability: it is important when there exist large training data sets, huge number of rules and long pattern rules. The efficiency and accuracy typically affect each ot ...
“Clustering Algorithm Employ in Web Usage Mining”: An Overview
... clustering tendency, to try to guess if clusters are present at all; note that any clustering algorithm will produce some clusters regardless of whether or not natural clusters exist [9][10]. 5.2 Clustering Algorithm: 5.2.1 Hierarchical algorithms: HA provide a hierarchical grouping of the objects. ...
... clustering tendency, to try to guess if clusters are present at all; note that any clustering algorithm will produce some clusters regardless of whether or not natural clusters exist [9][10]. 5.2 Clustering Algorithm: 5.2.1 Hierarchical algorithms: HA provide a hierarchical grouping of the objects. ...
Performance Evaluation of Students with Sequential Pattern Mining
... First process in any mining task is data collection. This data containing with student records with his/her all marks from S.S.C. to engineering. In data preprocessing we have to classify students according to branch and academic year. From these the student’s id and class they have obtained in each ...
... First process in any mining task is data collection. This data containing with student records with his/her all marks from S.S.C. to engineering. In data preprocessing we have to classify students according to branch and academic year. From these the student’s id and class they have obtained in each ...
CSE 142-6569
... showed that the sampling based technique can solve the problems using a sample whose size is in dependent of the number of transactions and the number of items as well. An extended association rule mining method was proposed by Shuji Morisaki etal. [23] that take advantage of interval and ratio scal ...
... showed that the sampling based technique can solve the problems using a sample whose size is in dependent of the number of transactions and the number of items as well. An extended association rule mining method was proposed by Shuji Morisaki etal. [23] that take advantage of interval and ratio scal ...
