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Mining Maximal Sparse Interval

Naba JyotiSarmah, Anjana Kakoti Mahanta
2012 International Journal of Computer Applications  
Mining frequent intervals from such data allow us to group the transactions with similar behavior. Similar to frequent intervals, mining sparse intervals are also important.  ...  In this paper we define the notion of sparse and maximal sparse interval and also propose an algorithm for mining maximal sparse intervals.  ...  The proposed method depends on a maximal frequent interval mining algorithm to mine maximal sparse intervals and the method for mining maximal frequent interval proposed in [6] is an O(n 2 ) algorithm  ... 
doi:10.5120/9281-3472 fatcat:5p27ylgim5fyde7npzj2gpefke

An Efficient Algorithm for Mining Maximal Sparse Interval from Interval Dataset

Naba JyotiSarmah, Anjana Kakoti Mahanta
2014 International Journal of Computer Applications  
A few numbers of data mining approaches have been developed to discover frequent intervals from interval datasets.  ...  We present an efficient algorithm with a worst case time complexity of O(n log n) for mining maximal sparse intervals. General Terms Data Mining, Algorithm.  ...  For this the list of maximal frequent intervals has to be known beforehand and for mining maximal frequent intervals it takes O(n 2 ) time and mining maximal sparse intervals from the set of maximal frequent  ... 
doi:10.5120/18838-0374 fatcat:gdbkk2ztozg75hpz7l4kgfegg4

Mining Minimal Infrequent Intervals

D. I., D. K., M. Dutta
2018 International Journal of Computer Applications  
In [1], the notion of mining maximal frequent interval in either a discrete domain or continuous domain is introduced.  ...  Mining infrequent intervals from such data allows users to group transactions with less similarity while mining frequent interval allows user to group the transaction with a similarity above a certain  ...  The problem of mining maximal frequent interval is to discover all the frequent intervals that are maximal and the problem of mining minimal infrequent intervals is to discover all the infrequent intervals  ... 
doi:10.5120/ijca2018916795 fatcat:kjupthaf6zfgvf5fnixhojk26i

Distributed Sequential Pattern Mining: A Survey and Future Scope

Suhasini Itkar, Uday Kulkarni
2014 International Journal of Computer Applications  
Different types of sequential pattern mining can be performed are sequential patterns, maximal sequential patterns, closed sequences, constraint based and time interval based sequential patterns.  ...  Distributed sequential pattern mining is the data mining method to discover sequential patterns from large sequential database on distributed environment.  ...  , maximal patterns and time interval based patterns.  ... 
doi:10.5120/16461-6187 fatcat:7ggeuyoqwnfzhnhwjixufttpua

Can we Take Advantage of Time-Interval Pattern Mining to Model Students Activity?

Oriane Dermy, Armelle Brun
2020 Educational Data Mining  
Finally, we show that time-interval pattern mining brings additional information compared to sequential pattern mining.  ...  Experiments reveal that frequent time-interval patterns are actually identified, which means that some students' activities are regulated not only by the order of learning resources but also by time.  ...  For each of them, the number of intervals, the maximal horizon, the fitting of the set, the frequency of each frequent interval, as well as the number of frequent patterns, are displayed.  ... 
dblp:conf/edm/DermyB20 fatcat:r6vpjguxd5cmrdntbbzbhm4x4u

Procedural Steps for Knowledge Mining in Time Series

Kaustuva ChandraDev, Sibananda Behera
2013 International Journal of Computer Applications  
Symbolic intervals which form temporal patterns are usually formulated through Allen's interval relations that originate in temporal reasoning.  ...  We present mining procedural steps which are more efficient, effective and based on item set techniques.  ...  Since it is similar to the mining of closed frequent item sets, we therefore follow the CHARM [12] algorithm.  ... 
doi:10.5120/10525-5508 fatcat:74slcxik6jbnzk6zzkw7nylp4m

Discovery of temporal association rules with hierarchical granular framework

Tzung-Pei Hong, Guo-Cheng Lan, Ja-Hwung Su, Pei-Shan Wu, Shyue-Liang Wang
2016 Applied Computing and Informatics  
However, an infrequent item for the entire time may be frequent within part of the time. We thus organize time into granules and consider temporal data mining for different levels of granules.  ...  Most of the existing studies in temporal data mining consider only lifespan of items to find general temporal association rules.  ...  In this paper, we consider the phenomenon that an itemset may not be frequent in the entire time interval, but may be frequent in a partial time interval.  ... 
doi:10.1016/j.aci.2016.01.003 fatcat:fyvkkakwgvdxvozy52h6wpo6ji

Efficient mining of understandable patterns from multivariate interval time series

Fabian Mörchen, Alfred Ultsch
2007 Data mining and knowledge discovery  
The search for coincidence and partial order in interval data can be formulated as instances of the well known frequent itemset problem.  ...  We define the Time Series Knowledge Representation (TSKR) as a new language for expressing temporal knowledge in time interval data.  ...  We show how important steps of the mining can be formulated as well-known problems from frequent itemset and sequential pattern mining.  ... 
doi:10.1007/s10618-007-0070-1 fatcat:j2ijrwv6tnag3gzeqypuhyn36i

Interestingness measure for mining sequential patterns in sports

Goran Hrovat, Iztok Fister, Katsiaryna Yermak, Gregor Stiglic, Iztok Fister
2015 Journal of Intelligent & Fuzzy Systems  
Essentially, the main novelty of the proposed method is significance testing for trends that serve as interestingness measures for mined sequential patterns.  ...  Using this method, the transformed time series data are exploited by a sequential pattern mining algorithm, then the novel trend of interestingness measures are calculated for discovering sequential patterns  ...  The first and third five minutes intervals contain low levels of heart-rate standard deviation and the second five minutes interval contains low levels of maximal speed.  ... 
doi:10.3233/ifs-151676 fatcat:gbgkzuvei5ccnkssls2lk4vlpa

Discovering the relationships between yarn and fabric properties using association rule mining

2017 Turkish Journal of Electrical Engineering and Computer Sciences  
This article also proposes two novel concepts, closed frequent item and maximal frequent item, to identify significant items in data.  ...  This study extracts different types of frequent itemsets (closed, maximal, top-k, top-k closed, top-k maximal) that have not been determined in textile sector before.  ...  Step 3: Maximal frequent itemsets Itemsets {D} and {B, C, E} are maximal frequent itemsets because none of their supersets are frequent.  ... 
doi:10.3906/elk-1611-16 fatcat:kamwnrvfbrao3dtvvwbpjgv33u

Optimal Discretization of Quantitative Attributes for Association Rules [chapter]

Stefan Born, Lars Schmidt-Thieme
2004 Classification, Clustering, and Data Mining Applications  
In 1996 Srikant and Agrawal formulated an information loss measure called measure of partial completeness and claimed that equidepth partitioning (i.e. discretization based on base intervals of equal support  ...  into l intervals we define the (continuous) measure of partial completeness as the maximal relative increase of length for all frequent intervals, i. e.  ...  F is the set of all frequent intervals.  ... 
doi:10.1007/978-3-642-17103-1_28 fatcat:tkqg6oapifae7fjh5cwbbjb3da

SPEED : Mining Maxirnal Sequential Patterns over Data Strearns

Chedy Raissi, Pascal Poncelet, Maguelonne Teisseire
2006 2006 3rd International IEEE Conference Intelligent Systems  
At any time, users can issue requests for frequent maximal sequences over an arbitrary time interval.  ...  In this paper we propose a new approach, called Speed (Sequential Patterns Efficient Extraction in Data streams), to identify frequent maximal sequential patterns in a data stream.  ...  If we now consider the time interval [0 − 2], i.e. batches B 1 0 and B 2 1 , maximal frequent patterns are: < (1)(2) >.  ... 
doi:10.1109/is.2006.348478 fatcat:5wmsplbwmfai7hanp4jt7ktcma

Mining Target-Oriented Sequential Patterns With Time-Intervals

Hao-En Chueh
2010 International Journal of Computer Science & Information Technology (IJCSIT)  
Clustering analysis is used with typical sequential pattern mining algorithm to extract the sequential patterns with time-intervals between successive itemsets.  ...  A time-interval sequential pattern is a sequential pattern with time-intervals between every pair of successive itemsets.  ...  et al. [22] use three predefined restrictions, the maximum interval are used to indicate the maximal and the minimal interval within subsequence respectively.  ... 
doi:10.5121/ijcsit.2010.2410 fatcat:dzlbmsjjwzf4voeyjgir77ourm

On the Sequential Pattern and Rule Mining in the Analysis of Cyber Security Alerts

Martin Husák, Jaroslav Kašpar, Elias Bou-Harb, Pavel Čeleda
2017 Proceedings of the 12th International Conference on Availability, Reliability and Security - ARES '17  
In this paper, we discuss usability of sequential pattern and rule mining, a subset of data mining methods, in an analysis of cyber security alerts.  ...  However, data mining is not always used to its full potential among cyber security community.  ...  A pattern is called frequent maximal pattern if there is no supersequence that is also frequent [14] .  ... 
doi:10.1145/3098954.3098981 dblp:conf/IEEEares/HusakKBC17 fatcat:kb7apfdmabc5nbfkma7uvvkcha

Efficient Analysis of Pattern and Association Rule Mining Approaches

Thabet Slimani, Amor Lazzez
2014 International Journal of Information Technology and Computer Science  
The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules.  ...  algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining.  ...  segments for maximal frequent itemsets.  ... 
doi:10.5815/ijitcs.2014.03.09 fatcat:azuo5zey35flrc3disyiersjnu
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