insode-2016 abstracts book - Awer
... Data mining is a matter of accessing to the information from large-scale data. Nowadays, increasing number of data, the widespread use of computers and steps towards becoming an information society increases the importance of data mining. In this study, software developed to perform data analysis wi ...
... Data mining is a matter of accessing to the information from large-scale data. Nowadays, increasing number of data, the widespread use of computers and steps towards becoming an information society increases the importance of data mining. In this study, software developed to perform data analysis wi ...
A Lattice Algorithm for Data Mining
... cept lattice algorithms fits in such data. Conclusions could help to build efficient ML algorithm based on concept lattice. When generating concepts, lattice algorithm focusses on objects or attributes. So if the number of objects is greater than the number of attributes, it might be interesting to ...
... cept lattice algorithms fits in such data. Conclusions could help to build efficient ML algorithm based on concept lattice. When generating concepts, lattice algorithm focusses on objects or attributes. So if the number of objects is greater than the number of attributes, it might be interesting to ...
Print this article - Informatics Journals
... to know about user behavior. In paper [3] the algorithms proposed have done the preprocessing activities for reducing the size of the log file and to identify the number of unique users and sessions. According to paper [1] intelligent system web usage preprocessor categorize human and search engine ...
... to know about user behavior. In paper [3] the algorithms proposed have done the preprocessing activities for reducing the size of the log file and to identify the number of unique users and sessions. According to paper [1] intelligent system web usage preprocessor categorize human and search engine ...
Distributed Scalable Collaborative Filtering Algorithm
... techniques. Matrix factorization [17] and correlation [5] based techniques are computationally expensive hence cannot deliver soft real-time CF. Further, in matrix factorization based approaches, updates to the input ratings matrix leads to non-local changes which leads to higher computational cost ...
... techniques. Matrix factorization [17] and correlation [5] based techniques are computationally expensive hence cannot deliver soft real-time CF. Further, in matrix factorization based approaches, updates to the input ratings matrix leads to non-local changes which leads to higher computational cost ...
ch0-695-F07 - Department of Computing Science
... To provide an introduction to knowledge discovery in databases and complex data repositories, and to present basic concepts relevant to real data mining applications, as well as reveal important research issues germane to the knowledge discovery domain and advanced mining applications. ...
... To provide an introduction to knowledge discovery in databases and complex data repositories, and to present basic concepts relevant to real data mining applications, as well as reveal important research issues germane to the knowledge discovery domain and advanced mining applications. ...
Finding Frequent Items in Data Streams
... Counter-based algorithms: Frequent, LossyCounting, SpaceSaving – Sketch algorithms: Count-Min Sketch, Count Sketch ...
... Counter-based algorithms: Frequent, LossyCounting, SpaceSaving – Sketch algorithms: Count-Min Sketch, Count Sketch ...
Parallel Outlier Detection on Uncertain Data for GPUs
... A considerable portion of data in the real world contains some degree of uncertainty [3], due to factors such as limitations in measuring equipment, partial responses or interpolation [5] [13]. An example of this could be a remote sensor network or location-based tracking system [42]. There have bee ...
... A considerable portion of data in the real world contains some degree of uncertainty [3], due to factors such as limitations in measuring equipment, partial responses or interpolation [5] [13]. An example of this could be a remote sensor network or location-based tracking system [42]. There have bee ...
CS490D: Introduction to Data Mining Chris Clifton
... Safety Board (NTSB) and the Federal Aviation Administration (FAA) • Integrating data from different sources as well as mining for patterns from a mix of both structured fields and free text is a difficult task • The goal of our initial analysis is to determine how data mining can be used to improve ...
... Safety Board (NTSB) and the Federal Aviation Administration (FAA) • Integrating data from different sources as well as mining for patterns from a mix of both structured fields and free text is a difficult task • The goal of our initial analysis is to determine how data mining can be used to improve ...
a scrutiny of association rule mining algorithms
... Computational experiments are performed to test the VNS algorithm against a benchmark problem set. The results show that the VNS algorithm is an effective approach for solving the MTFWS problem, capable of discovering many large-one frequent itemset with time-windows (FITW) with a larger timecoverag ...
... Computational experiments are performed to test the VNS algorithm against a benchmark problem set. The results show that the VNS algorithm is an effective approach for solving the MTFWS problem, capable of discovering many large-one frequent itemset with time-windows (FITW) with a larger timecoverag ...
Agents and Data Mining Interaction - CS
... Integration of agents and WfMS have been introduced in the late 1990s. Pioneer work [7, 8] argued that agents are suitable for workflows since the nature of the requirements could always evolve over time. Therefore, automatic process improvement is desirable, which can then intelligently adapt to th ...
... Integration of agents and WfMS have been introduced in the late 1990s. Pioneer work [7, 8] argued that agents are suitable for workflows since the nature of the requirements could always evolve over time. Therefore, automatic process improvement is desirable, which can then intelligently adapt to th ...
Service Learning Outcomes in an Undergraduate Data Mining Course
... that the business case for federal data mining efforts has not been made.[2] Netflix has recently announced a contest to improve the accuracy of their current selection prediction algorithms using data mining.[3,4] Data mining can even be applied to the NBA draft process![5] Data miners are highly s ...
... that the business case for federal data mining efforts has not been made.[2] Netflix has recently announced a contest to improve the accuracy of their current selection prediction algorithms using data mining.[3,4] Data mining can even be applied to the NBA draft process![5] Data miners are highly s ...
R Package clicksteam: Analyzing Clickstream Data with Markov
... showing the frequencies of the possible states. R> plot(mc, order = 2) ...
... showing the frequencies of the possible states. R> plot(mc, order = 2) ...
Insurance Fraud - Opal Consulting, LLC
... Randomness dissipates over time or with further knowledge. Fuzziness: Measures vagueness in language; Measures extent to which event occurs; and Vagueness does not dissipate with time or further knowledge. ...
... Randomness dissipates over time or with further knowledge. Fuzziness: Measures vagueness in language; Measures extent to which event occurs; and Vagueness does not dissipate with time or further knowledge. ...
A comparative study on principal component analysis and
... From the definition it can be mentioned that the support of an item is a statistical significance of an association rule. Suppose the support of an item is 0.1%, it means only 0.1 percent of the transaction contains purchasing of this item. The retailer will not pay much attention to such kind of it ...
... From the definition it can be mentioned that the support of an item is a statistical significance of an association rule. Suppose the support of an item is 0.1%, it means only 0.1 percent of the transaction contains purchasing of this item. The retailer will not pay much attention to such kind of it ...
data streams: models and algorithms
... field, practitioners and researchers may often find it an arduous task of isolating the right literature for a given topic. In addition, from a practitioners point of view, the use of research literature is even more difficult, since much of the relevant material is buried in publications. While han ...
... field, practitioners and researchers may often find it an arduous task of isolating the right literature for a given topic. In addition, from a practitioners point of view, the use of research literature is even more difficult, since much of the relevant material is buried in publications. While han ...
A Middleware for Developing Parallel Data Mining Applications
... tasks efficiently in parallel, starting from a relatively high-level specification of the technique. Our middleware is particularly suited for a cluster of SMP workstations, which have emerged as a cost-effective and common parallel computing environment in recent years. Our middleware performs parallel ...
... tasks efficiently in parallel, starting from a relatively high-level specification of the technique. Our middleware is particularly suited for a cluster of SMP workstations, which have emerged as a cost-effective and common parallel computing environment in recent years. Our middleware performs parallel ...
An integrated platform for spatial data mining and
... dynamically generated maps, greatly enhancing visual exploratory data analysis ([1 ], [3], [6], [15]). While being an exciting development for automating cartography, these systems have limited capabilities to visualize attribute interaction on a map having more than a few dimensions. Hence, complex ...
... dynamically generated maps, greatly enhancing visual exploratory data analysis ([1 ], [3], [6], [15]). While being an exciting development for automating cartography, these systems have limited capabilities to visualize attribute interaction on a map having more than a few dimensions. Hence, complex ...
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.