Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
Association rule mining is one of the most relevant techniques in data mining, aiming to extract correlation among sets of items or products in transactional databases. The huge number of association rules extracted represents the main obstacle that a decision maker faces. Hence, many interestingness measures have been proposed to evaluate the association rules. However, the abundance of these measures...
In Association rules mining, the task of finding frequent itemsets in dynamic database is very important because the updates may not only invalidate some existing rules but also make other rules relevant. In this paper, we propose a new algorithm to maintain frequent itemsets of a dynamic database in the case of record insertion as well as deletion simultaneously. Basically, the proposed algorithm...
Data mining involves discovering interesting patterns from large dataset to maximize the profit of the future business. Association rule mining is the main area in the field of data mining exploration with wide range of applications. Determining the frequent item-sets in large dataset is the core task of association rule mining and it is frequently used by business decision makers to improve their...
A new algorithm, named SCan-MAX for mining distributed maximal frequent itemsets from databases was proposed, the SCan-MAX used Sorted SCan-tree to store all the information of the transactions from the databases. SCan-MAX firstly scanned the local database and then gets the global 1-frequent itemsets, and then it created the SCan-tree on each node and used orderly sequence to store frequent itemsets...
PPC-Tree and N-list have been proven to be very efficient and been used in mining frequent itemsets widely. The main problem of the novel structures is that the way of First Constructing Last Encoding method is adopted in the tree-building phase. This causes excessive time consumption to mine frequent itemsets. In this paper, we propose SFO-Set based on SFO-Tree, a more efficient data structure, to...
Power grid dispatching has involved large amount of multi-source heterogeneous data with high complexity, therefore transforming power data into knowledge by data mining is the inevitable trend of intelligent dispatching development. Applying big data technology to power grid dispatching, the analysis model of multi-source heterogeneous data based on big data is built and a frequent itemset mining...
Data mining examines large pre-existing databases in order to generate new information. There are various tasks included under Data mining and association rule mining is considered as one of the crucial tasks among its. They are in form of if-then kind of statements which help to find relationships among huge data which do not hold relationship with each other within a relational database or any other...
There has been a growing need to automatically identify, extract and analyze risk related statements from textual data. In this paper, we have exploited natural language processing research to develop a risk analytics framework that processes human-reported risk statements to analyzes the enterprise risk description texts to classify them into valid and invalid risk categories, and perform analytics...
Klout, a famous App, could measure people's social network influence power. Klout score is measured according to the data from past 90 days and an individual who has high Klout score is thought as having high social influence power. Lots of businesses or organizations like to hire high Klout score people to help them to diffuse their brand images. However, Klout score cannot tell us who has high influence...
In this paper, we propose the WUN-set (Weighted Utility Nodeset) structure, an extension of the Nodeset structure, to solve the problem of mining frequent weighted utility itemsets from a quantitative database. Firstly, some theorems are developed to compute quickly the weighted utility support of an itemset. An algorithm is then proposed for the fast mining frequent weighted utility itemsets. The...
Sohrabi and Barforoush proposed the BVBUC (Bitwise vertical bottom up colossal) algorithm for mining colossal patterns based on a bottom up scheme. It, however, spends more time to check subsets and supersets, because it generates a lot of candidates and consumes more memory usage to store these. In this paper, we propose a new method for mining colossal patterns. Firstly, the CP (Colossal Pattern)-tree...
Visual analytics on frequent web usage patterns aims to help users to (i) analyze the data so as to discover implicit, previously unknown and potentially useful information in the form of collections of frequently visited web pages in a single session and to (ii) visually represent the discovered knowledge so as to gain insight about the data. In this paper, we propose an interactive visual analytics...
The step of mining frequent itemsets in database is the essential step and most expensive in the process of mining association rules in data mining task, many algorithms of mining frequent itemsets have been proposed to improve the performance of Apriori Algorithm. In this paper, we have introduced an optimization in the phase of generation pruning of candidates by a new strategy for the calculation...
Understanding customer buying patterns is of great interest to the retail industry. Association rule mining is a common technique for extracting correlations such as people in the South of France buy rosé wine or customers who buy paté also buy salted butter and sour bread. Unfortunately, sifting through a high number of buying patterns is not useful in practice, because of the predominance of popular...
Planning has achieved significant progress in recent years. Among the various approaches to scale up plan synthesis, the use of macro-actions has been widely explored. As a first stage towards the development of a solution to learn on-line macro-actions, we propose an algorithm to identify useful macroactions based on data mining techniques. The integration in the planning search of these learned...
The main objective is to address the possibility of designing a similarity measure and using the same to find temporal similarity between patterns in temporal databases which are time stamped. The proposed similarity measure is a function of threshold and deviation. Given, a reference pattern which is of user interest, unearthing eccentric temporal association patterns requires an efficient similarity...
In many applications and situations, there is a need to estimate which temporal patterns are similar from a temporal database at various timestamps. In such a case, we have to obviously determine true supports of these patterns. If we can somehow reduce number of times, true support is found then it reduces cost of computation. This is the actual thought behind this work. In this work, we achieve...
Sequences are one of the most important types of patterns which are extracted from datasets and are used to construct association rules. Several sequential pattern mining methods have been proposed in the literature. This paper introduces a novel bit wise approach to compress and represent the sequence database as a 3-dimentional array and use a corresponding mining method to extract frequent sequences...
Outlier Detection is one of the important research problems in temporal data mining. A pattern in time stamped temporal database is a sequence of probability values. Finding outliers from time stamped temporal databases requires suitable dissimilarity measure to find dissimilarity between input pattern and reference pattern which is of user interest. In the current work, the objective is to propose...
As part of their design, card games often include information that is hidden from opponents and represents a strategic advantage if discovered. A player that can discover this information will be able to alter their strategy based on the nature of that information, and therefore become a more competent opponent. In this paper, we employ association rule-mining techniques for predicting item multisets,...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.