Stock Control using Data Mining - International Journal of Computer
... “Data Mining is finding interesting structure in databases.”(U.Fayyad,S.Chaudhari and P.Bradley)[1].With this Approach strong association rules also can be generated. ...
... “Data Mining is finding interesting structure in databases.”(U.Fayyad,S.Chaudhari and P.Bradley)[1].With this Approach strong association rules also can be generated. ...
Efficient and Effective Clustering Methods for Spatial Data Mining
... of diseases), chemistry (grouping of compounds), social stud& (claseification of statistical findings), and so on. Its main goal is to identify structures or clusfers present in the data. While there is no general definition of a cluster, algorithms have been developed to find several kinds of clust ...
... of diseases), chemistry (grouping of compounds), social stud& (claseification of statistical findings), and so on. Its main goal is to identify structures or clusfers present in the data. While there is no general definition of a cluster, algorithms have been developed to find several kinds of clust ...
Privacy-Preserving Data Visualization using Parallel Coordinates
... (Figure 1a). This is an area where a lot of work has been done in the field of PPDM. The main techniques for sanitization are a) randomization, where random noise is added to the data for the purpose of perturbation, b) suppression, where some values are hidden and c) generalization, where data valu ...
... (Figure 1a). This is an area where a lot of work has been done in the field of PPDM. The main techniques for sanitization are a) randomization, where random noise is added to the data for the purpose of perturbation, b) suppression, where some values are hidden and c) generalization, where data valu ...
An Efficient Algorithm for Mining Association Rules for Large
... Ck, the proposed method does not scan the whole database. Instead, only the compact table which represents the database To evaluate the efficiency of the proposed algorithm, we transactions is accessed, and the count is obtained directly have extensively studied our method performance by without the ...
... Ck, the proposed method does not scan the whole database. Instead, only the compact table which represents the database To evaluate the efficiency of the proposed algorithm, we transactions is accessed, and the count is obtained directly have extensively studied our method performance by without the ...
Clustering Product Features for Opinion Mining
... for calculating the similarity between two words wk and wq given in [16]. We also tried some other similarity calculation algorithms Res [36] and Lin [23], but Jcn performs the best for our task. These measures all rely on varying degrees of least common subsumer (LCS), which is the most specific co ...
... for calculating the similarity between two words wk and wq given in [16]. We also tried some other similarity calculation algorithms Res [36] and Lin [23], but Jcn performs the best for our task. These measures all rely on varying degrees of least common subsumer (LCS), which is the most specific co ...
Mining Frequent ItemSet Based on Clustering of Bit Vectors
... called Bit Table to generate candidate itemsets and compress the database. The issue of finding frequent itemsets which can be obtained solution by building a candidate set generation and then distinguishing itemsets which satisfy the requirement of the frequent itemset inside candidate set. To red ...
... called Bit Table to generate candidate itemsets and compress the database. The issue of finding frequent itemsets which can be obtained solution by building a candidate set generation and then distinguishing itemsets which satisfy the requirement of the frequent itemset inside candidate set. To red ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.