Efficient Mining of Partial Periodic Patterns with Individual Event Support Thresholds Using Minimum Constraints

2014 ◽  
Vol 22 (06) ◽  
pp. 793-814
Author(s):  
Kung-Jiuan Yang ◽  
Tzung-Pei Hong ◽  
Guo-Cheng Lan ◽  
Yuh-Min Chen
Keyword(s):  
Real World ◽  
Execution Time ◽  
Real Life ◽  
Individual Event ◽  
Periodic Patterns ◽  
Minimum Support ◽  
Real World Applications ◽  
Support Threshold ◽  
Multiple Minimum Supports ◽  
Minimum Constraints

Partial periodic patterns are commonly seen in real-life applications and provide useful prediction with uncertainty. Most previous approaches have set a single minimum support threshold for all events to assume they have similar frequencies which is not practical for real-world applications. Instead of setting a single minimum support threshold for all events, Chen et al. proposed an FP-tree-like algorithm to allow multiple minimum supports for reflecting the natures of the events. However, such a tree-based algorithm encountered an efficiency problem while period length is long or event sequential orders in period segments are varied. Under the circumstance, many tree branches are created and much execution time is spent to find partial periodic patterns. In this paper, we thus propose a projection-based algorithm which examines only prefix subsequences and projects only corresponding postfix subsequences with multiple minimum supports to quickly find the partial periodic patterns in a recursive process. Experiments on both synthetic and real-life datasets show that the proposed algorithm is more efficient than the previous one.

An Efficient Pruning and Filtering Strategy to Mine Partial Periodic Patterns from a Sequence of Event Sets

2014 ◽  
Vol 10 (2) ◽  
pp. 18-38 ◽  
Author(s):  
Kung-Jiuan Yang ◽  
Tzung-Pei Hong ◽  
Yuh-Min Chen ◽  
Guo-Cheng Lan
Keyword(s):  
Time Series ◽  
Real World ◽  
Oil Price ◽  
Time Stamp ◽  
Single Event ◽  
Periodic Patterns ◽  
Event Time ◽  
The Past ◽  
Real World Applications ◽  
Speed Up

Partial periodic patterns are commonly seen in real-world applications. The major problem of mining partial periodic patterns is the efficiency problem due to a huge set of partial periodic candidates. Although some efficient algorithms have been developed to tackle the problem, the performance of the algorithms significantly drops when the mining parameters are set low. In the past, the authors have adopted the projection-based approach to discover the partial periodic patterns from single-event time series. In this paper, the authors extend it to mine partial periodic patterns from a sequence of event sets which multiple events concurrently occur at the same time stamp. Besides, an efficient pruning and filtering strategy is also proposed to speed up the mining process. Finally, the experimental results on a synthetic dataset and real oil price dataset show the good performance of the proposed approach.

An Efficient Approach for Incremental Mining Fuzzy Frequent Itemsets with FP-Tree

2016 ◽  
Vol 24 (03) ◽  
pp. 367-386 ◽  
Author(s):  
Weigang Huo ◽  
Xingjie Feng ◽  
Zhiyuan Zhang
Keyword(s):  
Execution Time ◽  
Frequent Itemsets ◽  
Incremental Mining ◽  
Minimum Support ◽  
Efficient Approach ◽  
Support Threshold ◽  
Dynamic Databases ◽  
Norm Operator ◽  
Memory Cost

Keeping the generated fuzzy frequent itemsets up-to-date and discovering the new fuzzy frequent itemsets are challenging problems in dynamic databases. In this paper, the classical H-struct structure is extended to mining fuzzy frequent itemsets. The extended H-mine algorithm can use any t-norm operator to calculate the support of fuzzy itemset. The FP-tree-based structure called the Initial-FP-tree and the New-FP-tree are built to maintain the fuzzy frequent itemsets in the original database and the new inserted transactions respectively. The strategy of incremental mining of fuzzy frequent itemsets is achieved by breath-first-traversing the Initial-FP-tree and the New-FP-tree. All of the fuzzy frequent itemsets in the updated database can be obtained by traversing the Initial-FP-tree. The experiments on real datasets show that the proposed approach runs faster than the batch extended H-mine algorithm. Comparing with the existing algorithm for incremental mining fuzzy frequent itemsets, the proposed approach is superior in terms of the execution time. The memory cost of the proposed approach is lower than that of the existing algorithm when the minimum support threshold is low.

E-MsNFIS: Efficient Negative Frequent Itemsets Mining Based on Multiple Minimum Supports

2013 ◽  
Vol 411-414 ◽  
pp. 386-389 ◽  
Author(s):  
Tian Tian Xu ◽  
Xiang Jun Dong
Keyword(s):  
Association Rules ◽  
Real Life ◽  
Frequent Itemsets ◽  
Negative Association ◽  
Experimental Results ◽  
New Method ◽  
Minimum Support ◽  
Frequent Itemsets Mining ◽  
Negative Association Rules ◽  
Multiple Minimum Supports

Negative frequent itemsets (NFIS) like (a1a2¬a3a4) have played important roles in real applications because we can mine valued negative association rules from them. In one of our previous work, we proposed a method, namede-NFISto mine NFIS from positive frequent itemsets (PFIS). However,e-NFISonly uses single minimum support, which implicitly assumes that all items in the database are of the same nature or of similar frequencies in the database. This is often not the case in real-life applications. So a lot of methods to mine frequent itemsets with multiple minimum supports have been proposed. These methods allow users to assign different minimum supports to different items. But these methods only mine PFIS, doesn’t consider negative ones. So in this paper, we propose a new method, namede-msNFIS, to mine NFIS from PFIS based on multiple minimum supports. E-msNFIScontains three steps: 1) using existing methods to mine PFIS with multiple minimum supports; 2) using the same method ine-NFISto generate NCIS from PFIS got in step 1; 3) calculating the support of these NCIS only using the support of PFIS and then gettingNFIS. Experimental results show that thee-msNFISis efficient.

E-msNSP: Efficient Negative Sequential Patterns Mining Based on Multiple Minimum Supports

2017 ◽  
Vol 31 (02) ◽  
pp. 1750003 ◽  
Author(s):  
Tiantian Xu ◽  
Xiangjun Dong ◽  
Jianliang Xu ◽  
Yongshun Gong
Keyword(s):  
Real Life ◽  
Search Space ◽  
Research Area ◽  
Sequential Patterns ◽  
Minimum Support ◽  
Related Information ◽  
Negative Item ◽  
Important Research Area ◽  
Set Up ◽  
Multiple Minimum Supports

Mining negative sequential patterns (NSP) has been an important research area in data mining and knowledge discovery and it is much more challenging than mining positive sequential patterns (PSP) due to the computational complexity and search space. Only a few methods have been proposed to mine NSP and most of them only use single minimum support, which implicitly assumes that all items in the database are of the same nature or of similar frequencies in the database. This is often not the case in real-life applications. There are several methods to mine sequential patterns with multiple minimum supports (MMS), but these methods only consider PSP and do not handle NSP. So in this paper, we propose a new method, called e-msNSP, to mine NSP with multiple minimum supports. We also solve the problem of how to set up the minimum support to a sequence with negative item(s). E-msNSP consists of three major steps: (i) using the improved MS-GSP method to mine PSP with multiple minimum supports and storing all positive sequential candidates’ (PSC) related information simultaneously; (ii) using the same method in e-NSP to generate negative sequential candidates (NSC) based on above mined PSP; (iii) calculating the support of these NSC based only on the corresponding PSP and then getting NSP. To the best of our knowledge, e-msNSP is the first method to mine NSP with MMS and does not impose strict constraints. Experimental results show that the e-msNSP is highly effective and efficient.

Real-World Applications of Periodic Patterns

2021 ◽  
pp. 229-235
Author(s):  
R. Uday Kiran ◽  
Masashi Toyoda ◽  
Koji Zettsu
Keyword(s):  
Real World ◽  
Periodic Patterns ◽  
Real World Applications

scholarly journals Enumerating Maximal k-Plexes with Worst-Case Time Guarantee

2020 ◽  
Vol 34 (03) ◽  
pp. 2442-2449
Author(s):  
Yi Zhou ◽  
Jingwei Xu ◽  
Zhenyu Guo ◽  
Mingyu Xiao ◽  
Yan Jin
Keyword(s):  
Community Detection ◽  
Real World ◽  
State Of The Art ◽  
Real Life ◽  
The State ◽  
Enumeration Algorithm ◽  
Worst Case ◽  
Running Time ◽  
Real World Applications ◽  
First Time

The problem of enumerating all maximal cliques in a graph is a key primitive in a variety of real-world applications such as community detection and so on. However, in practice, communities are rarely formed as cliques due to data noise. Hence, k-plex, a subgraph in which any vertex is adjacent to all but at most k vertices, is introduced as a relaxation of clique. In this paper, we investigate the problem of enumerating all maximal k-plexes and present FaPlexen, an enumeration algorithm which integrates the “pivot” heuristic and new branching schemes. To our best knowledge, for the first time, FaPlexen lists all maximal k-plexes with provably worst-case running time O(n2γn) in a graph with n vertices, where γ < 2. Then, we propose another algorithm CommuPlex which non-trivially extends FaPlexen to find all maximal k-plexes of prescribed size for community detection in massive real-life networks. We finally carry out experiments on both real and synthetic graphs and demonstrate that our algorithms run much faster than the state-of-the-art algorithms.

scholarly journals Linking Online Misuse-Resistant Authenticated Encryption and Blockwise Attack Models

2017 ◽  
pp. 125-144
Author(s):  
Guillaume Endignoux ◽  
Damian Vizár
Keyword(s):  
Real World ◽  
Real Life ◽  
Authenticated Encryption ◽  
Real World Applications ◽  
Encryption Schemes ◽  
Significant Line ◽  
Attack Models

Real-world applications of authenticated encryption often require the encryption to be computable online, e.g. to compute the ith block of ciphertext after having processed the first i blocks of plaintext. A significant line of research was dedicated to identifying security notions for online authenticated encryption schemes, that capture various security goals related to real-life scenarios. Fouque, Joux, Martinet and Valette proposed definitions of privacy and integrity against adversaries that can query their oracles in a blockwise-adaptive manner, to model memory-constrained applications. A decade later, Fleischmann, Forler and Lucks proposed the notion of online nonce misuse-resistant authenticated encryption (OAE) to capture the security of online authenticated encryption under nonce-reuse. In this work we investigate the relation between these notions. We first recast the blockwise notions of Fouque et al. to make them compatible with online authenticated encryption schemes that support headers. We then show that OAE and the conjunction of the blockwise notions are “almost” equivalent. We identify the missing property on the side of blockwise notions, and formalize it under the name PR-TAG. With PR-TAG being just an auxiliary definition, the equivalence we finally show suggests that OAE and the blockwise model for online authenticated encryption capture essentially the same notion of security.

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

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