Store, share & discover realtime sensor, energy and environment data from objects, devices & buildings around the world. Pachube is a convenient, secure & scalable platform that helps you connect to & build the 'internet of things'
This is an abstractive summarization demo program. It was mainly used to summarize opinions, but since it does not rely on any domain information, it can be used to summarize any highly redundant text.
Data Mining, Analytics, and Databases
Databases are the workhorse of the enterprise today. Searching through databases and finding useful information has become a big computational challenge. Researchers from academia and Microsoft, Oracle, SAP, and many other corporations are looking to CUDA-enabled GPUs to find a scalable solution.
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
by Andrew Moore (CMU), including tutorials on decision trees, information gain, cross validation, naive bayesian classifiers, hidden markov models, support vector machines, k-means and hierarchical clustering
Data mining (DM), also known as Knowledge-Discovery in Databases (KDD) or Knowledge-Discovery and Data Mining (KDD), is the process of automatically searching large volumes of data for patterns. Data mining is a fairly recent and contemporary topic in computer science. However, Data mining applies many older computational techniques from statistics, information retrieval, machine learning and pattern recognition.
Web search engines have changed our lives - enabling instant access to information about subjects that are both deeply important to us, as well as passing whims. The search engines that provide answers to our search queries also log those queries, in order to improve their algorithms. Academic research on search queries has shown that they can provide valuable information on diverse topics including word and phrase similarity, topical seasonality and may even have potential for sociology, as well as providing a barometer of the popularity of many subjects. At the same time, individuals are rightly concerned about what the consequences of accidental leaking or deliberate sharing of this information may mean for their privacy. In this talk I will cover the applications which have benefited from mining query logs, the risks that privacy can be breached by sharing query logs, and current algorithms for mining logs in a way to prevent privacy breaches.
In this paper, we introduce a diamond episode of the form s1 -> E -> s2, where s1 and s2 are events and E is a set of events. The diamond episode s1 -> E -> s2 means that every event of E follows an event s1 and is followed by an event s2. Then, by formulating the support of diamond episodes, in this paper, we design the algorithm FreqDmd to extract all of the frequent diamond episodes from a given event sequence. Finally, by applying the algorithm FreqDmd to bacterial culture data,we extract diamond episodes representing replacement of bacteria.
Discovering patterns with great significance is an important problem in data mining discipline. An episode is defined to be a partially ordered set of events for consecutive and fixed-time intervals in a sequence. Most of previous studies on episodes consider only frequent episodes in a sequence of events (called simple sequence). In real world, we may find a set of events at each time slot in terms of various intervals (hours, days, weeks, etc.). We refer to such sequences as complex sequences. Mining frequent episodes in complex sequences has more extensive applications than that in simple sequences. In this paper, we discuss the problem on mining frequent episodes in a complex sequence. We extend previous algorithm MINEPI to MINEPI+ for episode mining from complex sequences. Furthermore, a memory-anchored algorithm called EMMA is introduced for the mining task. Experimental evaluation on both real-world and synthetic data sets shows that EMMA is more efficient than MINEPI+.
In this paper, we introduce the class of k-partite episodes, which are time-series patterns of the form ?A1, . . . ,Ak? for sets Ai (1 = i = k) of events meaning that, in an input event sequence, every event of Ai is followed by every event of Ai+1 for every 1 = i < k. Then, we present a backtracking algorithm Kpar and its modification Kpar2 that find all of the frequent k-partite episodes from an input event sequence without duplication. By theoretical analysis, we show that these two algorithms run in polynomial delay and polynomial space in total input size.
Recently, knowledge discovery in large data increases its importance in various fields. Especially, data mining from time-series data gains much attention. This paper studies the problem of finding frequent episodes appearing in a sequence of events. We propose an efficient depth-first search algorithm for mining frequent serial episodes in a given event sequence using the notion of right-minimal occurrences. Then, we present some techniques for speeding up the algorithm, namely, occurrence-deliver and tail-redundancy pruning. Finally, we ran experiments on real datasets to evaluate the usefulness of the proposed methods.
CiteSeerX - Document Details (Isaac Councill, Lee Giles): Sequences of events describing the behavior and actions of users or systems can be collected in several domains. We consider the problem of discovering frequently occurring episodes in such sequences. An episode is defined to be a collection of events that occur relatively close to each other in a given partial order. Once such episodes are known, one can produce rules for describing or predicting the behavior of the sequence. We give efficient algorithms for the discovery of all frequent episodes from a given class of episodes, and present extensive experimental results. The methods are in use in telecommunication alarm management.
Event Sequence arises naturally in many applications. Episode mining can discovery the knowledge hidden in the event sequence. Currently, the most influential algorithm for episode mining is WINEPI. However, it is likely to suffer from the tendency of generating too many of candidate episodes. In this paper, a novel algorithm named DRE for mining frequent episodes is presented. It studied the conditions for the events which can be pruned from the database, so the size of database is reduced gradually. The performance of algorithm DRE was evaluated and compared with WINEPI algorithm. The results demonstrate that the DRE has better performance.
Wiki: A wiki is a website that uses wiki software, allowing the easy creation and editing of any number of interlinked (often databased) Web pages, using a simplified markup language. Wikis are often used to create collaborative...
This began on March 25, 1995. A little later (May 1, 1995), an InvitationToThePatternsList caused an increase in participation. Growth has continued since then, to the point where the average number of new pages ranges between 5 and 12 per day.
{. Schouten, {. Bueno, W. Duivesteijn, und M. Pechenizkiy. Data Mining and Knowledge Discovery, 36 (1):
379--413(Januar 2022)Funding Information: This research is supported by EDIC project funded by NWO. We thank the EDIC consortium and the ZGT hospital for allowing us to analyse the data from the DIALECT-2 study. We especially thank Niala Den Braber (PhD candidate at Universiteit Twente and researcher internal medicine at ZGT hospital) and prof. dr. Goos Laverman (internist-nephrologist at ZGT hospital) for giving us clinical valuation of our findings. In addition, we thank our colleagues dr. Robert Peharz for giving us useful insights on Markov chains and DBNs and dr. Maryam Tavakol for guiding us towards the MovieLens dataset..
B. Langholz, und D. Richardson. American journal of epidemiology, 171 (3):
377-83(Februar 2010)5491<m:linebreak></m:linebreak>JID: 7910653; aheadofprint;.
C. Lanzolla, G. Colasuonno, K. Milillo, und G. Caputo. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 8 (3):
01-12(August 2019)