Algorithm for Discovering Patterns in Sequences
... - use overlapping of R-Tree to represent successive states of database - if the number of moving objects from one time instant to another is large, the approach degenerates to independent tree structures and thus no paths are common ...
... - use overlapping of R-Tree to represent successive states of database - if the number of moving objects from one time instant to another is large, the approach degenerates to independent tree structures and thus no paths are common ...
On the Application of Data Mining to Official Data
... and patterns (Hand, 1998b). Building models is a major activity of many statisticians and econometricians, especially in NSI’s and it will not be necessary to elaborate too long on this. A model is a global summary of relationships between variables, which both helps to understand phenomena and allo ...
... and patterns (Hand, 1998b). Building models is a major activity of many statisticians and econometricians, especially in NSI’s and it will not be necessary to elaborate too long on this. A model is a global summary of relationships between variables, which both helps to understand phenomena and allo ...
A Survey on Frequent Pattern Mining Methods Apriori, Eclat, FP growth
... technology which is continuously increasing its importance in all the aspects of human life. As an important task of data mining, Frequent pattern Mining should understood by researchers to make modification in existing algorithms or to utilize algorithm and methods in more specific way to optimize ...
... technology which is continuously increasing its importance in all the aspects of human life. As an important task of data mining, Frequent pattern Mining should understood by researchers to make modification in existing algorithms or to utilize algorithm and methods in more specific way to optimize ...
HG3212991305
... the original k-means has sum-of-squared-error objective function that uses Euclidean distance. In a very sparse and high-dimensional domain like text documents, spherical k-means, which uses cosine similarity (CS) instead of Euclidean distance as the measure, is deemed to be more suitable .In, Baner ...
... the original k-means has sum-of-squared-error objective function that uses Euclidean distance. In a very sparse and high-dimensional domain like text documents, spherical k-means, which uses cosine similarity (CS) instead of Euclidean distance as the measure, is deemed to be more suitable .In, Baner ...
Machine learning
... Kind of heuristic: may be false specifically, suppose we look at all possible worlds Data hypothesis (+ new example) prediction In 50% of possible worlds this prediction will be false This holds for all learning methods; no method is on average better than any other method ...
... Kind of heuristic: may be false specifically, suppose we look at all possible worlds Data hypothesis (+ new example) prediction In 50% of possible worlds this prediction will be false This holds for all learning methods; no method is on average better than any other method ...
Data Mining
... Obvious way: compare 10fold CV estimates Generally sufficient in applications (we don't loose if the chosen method is not truly better) However, what about machine learning research? Need to show convincingly that a particular method works better ...
... Obvious way: compare 10fold CV estimates Generally sufficient in applications (we don't loose if the chosen method is not truly better) However, what about machine learning research? Need to show convincingly that a particular method works better ...
Data Mining and Data Warehousing
... as Journal of the American Society of Information Science, Library Trends and Information Technology in Libraries on data mining, text mining, and Web mining describing how libraries are using associated software for primarily administrative purposes. While these tools have shown a dramatic increase ...
... as Journal of the American Society of Information Science, Library Trends and Information Technology in Libraries on data mining, text mining, and Web mining describing how libraries are using associated software for primarily administrative purposes. While these tools have shown a dramatic increase ...
Ordered and Unordered Top-K Range Reporting in Large Data Sets
... is able to report the K smallest elements in the subsequence A[i, j] using O(logB N + K/B) I/Os, for any triple (i, j, K) with 1 ≤ i ≤ j ≤ N and 1 ≤ K ≤ j − i + 1. We take the following view of the problem. We denote the ith element in A by ai and consider the pair (i, ai ) to be a point in the plan ...
... is able to report the K smallest elements in the subsequence A[i, j] using O(logB N + K/B) I/Os, for any triple (i, j, K) with 1 ≤ i ≤ j ≤ N and 1 ≤ K ≤ j − i + 1. We take the following view of the problem. We denote the ith element in A by ai and consider the pair (i, ai ) to be a point in the plan ...
Knowledge Discovery and Data Mining - OPUS at UTS
... amount of information (or data) to facilitate this 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 e ...
... amount of information (or data) to facilitate this 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 e ...
Suffix Tree Clustering - Data mining algorithm
... terminates if a word starts with a vowel and there are only two letters left or if a word starts with a consonant and there are only three characters left [6]. Otherwise, the rule is applied and the process repeats. The advantage is its simple form and every iteration taking care of both deletion an ...
... terminates if a word starts with a vowel and there are only two letters left or if a word starts with a consonant and there are only three characters left [6]. Otherwise, the rule is applied and the process repeats. The advantage is its simple form and every iteration taking care of both deletion an ...
Design and Implementation of Improved Frequent Item Set
... set generation which involves repeated number of scans and when the database is large it causes increase in time and space complexity due to which the process is not obsolete. The Apriori algorithm [4] uses a bottom-up breadth-first approach to find the large item set. As it was proposed to grip the ...
... set generation which involves repeated number of scans and when the database is large it causes increase in time and space complexity due to which the process is not obsolete. The Apriori algorithm [4] uses a bottom-up breadth-first approach to find the large item set. As it was proposed to grip the ...
Hiding sensitive patterns in association rules mining
... knowledge. Thus, when a client sends its database to ...
... knowledge. Thus, when a client sends its database to ...
Mining Complex Data Streams - Journal of Advances in Information
... of cut points, which are categorized into top-down or bottom-up approach. Top-down discretization starts with a single interval that encompasses the entire value range, and then repeatedly splits it into sub-intervals until some stopping criterion is satisfied. It gives a list of k-1 boundary points ...
... of cut points, which are categorized into top-down or bottom-up approach. Top-down discretization starts with a single interval that encompasses the entire value range, and then repeatedly splits it into sub-intervals until some stopping criterion is satisfied. It gives a list of k-1 boundary points ...
Association Rule Mining -Various Ways: A Comprehensive Study
... logic of GA to improve the scenario of frequents itemsets data mining using association rule mining. The main benefit of using GA in frequent itemsets mining is to perform global search with less time complexity. This scheme gives better results in huge or larger data set. It is also simple and effi ...
... logic of GA to improve the scenario of frequents itemsets data mining using association rule mining. The main benefit of using GA in frequent itemsets mining is to perform global search with less time complexity. This scheme gives better results in huge or larger data set. It is also simple and effi ...
A Parallel Spatial Co-location Mining Algorithm
... massive tasks are becoming increasingly popular since the introduction of MapReduce programming model, and Hadoop’s run-time environment and distributed file systems [13]. For large-scale data processing on clusters of commodity machines, MapReduce-like systems have been proven to be an efficient fram ...
... massive tasks are becoming increasingly popular since the introduction of MapReduce programming model, and Hadoop’s run-time environment and distributed file systems [13]. For large-scale data processing on clusters of commodity machines, MapReduce-like systems have been proven to be an efficient fram ...
A Study on Spatial Data Mining
... appear to be very revolutionary compared with those applied to relational databases (automatic classification). The clustering is performed using a similarity function which was already classed as a semantic distance. Hence, in spatial databases it appears natural to use the Euclidean distance in or ...
... appear to be very revolutionary compared with those applied to relational databases (automatic classification). The clustering is performed using a similarity function which was already classed as a semantic distance. Hence, in spatial databases it appears natural to use the Euclidean distance in or ...
Improved Information Gain Estimates for Decision Tree
... the training set. To make predictions at test time, we simply take the majority decision over the individual predictions made by all trees in the ensemble. Setup. We use 30 data sets from a variety of sources. We use all multiclass (K ≥ 3) data sets from UCI, as well as four text classification data ...
... the training set. To make predictions at test time, we simply take the majority decision over the individual predictions made by all trees in the ensemble. Setup. We use 30 data sets from a variety of sources. We use all multiclass (K ≥ 3) data sets from UCI, as well as four text classification data ...
On the Number of Clusters in Block Clustering
... columns that exhibit a high correlation. A number of algorithms that perform simultaneous clustering on rows and columns of a matrix have been proposed to date. They have practical importance in a wide variety of applications such as biology, data analysis, text mining and web mining. A wide range o ...
... columns that exhibit a high correlation. A number of algorithms that perform simultaneous clustering on rows and columns of a matrix have been proposed to date. They have practical importance in a wide variety of applications such as biology, data analysis, text mining and web mining. A wide range o ...
Outlier detection in financial statements: a text mining
... promising. Following these developments, a number of network languages were employed to model the semantics of natural language and other domains. Some examples can be found in [11-14]. In our research we utilize the conceptual graph, a particular network language proposed by Sowa and Way [15] and s ...
... promising. Following these developments, a number of network languages were employed to model the semantics of natural language and other domains. Some examples can be found in [11-14]. In our research we utilize the conceptual graph, a particular network language proposed by Sowa and Way [15] and s ...
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.