scholarly journals A Report on Haui-Miner and Ehaupm Algorithms on Pattern Mining with Upper Limits

2019 ◽  
Vol 9 (1) ◽  
pp. 1252-1256
Keyword(s):  
Execution Time ◽  
Pattern Mining ◽  
Primary Target ◽  
Information Mining ◽  
Utility Mining ◽  
Memory Space ◽  
Relevant Item ◽  
Upper Limits ◽  
Achievement Measures ◽  
Utility Models

Utility-mining is the present developing discipline of information-mining. Utility-mining combines different structures such as High relevant item-set mining, Relevant successive item-set mining, Negative relevant item-set mining, Uncommon high relevant item-set mining and so forth. Each procedure of these item-sets mining doesn’t acknowledge length of item-sets. An ongoing improvement in the field of Utility-mining is high normal utility item-set mining. The normal Utility-mining deals with length of item- sets alongside the utility of item-sets. Here few calculations are introduced to recover high average relevant item-sets present in the database. Primary target of the present work was to look at the three High Normal Utility Models calculations:1)High Normal Utility Models (HAUP) calculation, 2)High Normal Utility Item-Set-Excavator (HAUI-Miner) Calculation and 3)Productive High Normal Utility Pattern-Mining (EHAUPM) calculation. The execution-time and memory-space are examined as achievement measures for correlation. The EHAUPM calculation is more efficient compared to other calculations; this is discovered from the performed analysis.

scholarly journals Practical Time Optimization of DSP Program for High-frequency Applications

2021 ◽  
Vol 2095 (1) ◽  
pp. 012005
Author(s):  
Zhuyu Xun ◽  
Hongfa Ding ◽  
Zhou He
Keyword(s):  
Execution Time ◽  
High Frequency ◽  
Rapid Development ◽  
Power Conversion ◽  
High Frequency Power ◽  
Memory Space ◽  
Time Optimization ◽  
The Cost ◽  
Frequency Power

Abstract The rapid development of the high frequency power conversion techniques makes great demands on the methods that can reduce the execution time of the program effectively. This paper is aiming at reducing the execution time of the program in several aspects such as sampling, complex expressions, and so on. As one of the most widely applied methods, reducing the execution time of the program at the cost of the memory space is adopted in this paper. Furthermore, in order to confirm the feasibility and superiority of programs that are proposed in this paper, they are compared with other programs that can realize the same function in terms of the execution time.

scholarly journals An Efficient and Fast Partial Template Matching Technique – Enhancement in Normalized Cross Correlation

2019 ◽  
Vol 9 (1) ◽  
pp. 7232-7237
Keyword(s):  
Image Processing ◽  
Execution Time ◽  
Template Matching ◽  
Absolute Difference ◽  
Processing Efficiency ◽  
Memory Space ◽  
Matching Technique ◽  
Speed Up ◽  
Novel Method ◽  
Window Approach

Template matching forms the basis of many image processing algorithms and hence the computer vision algorithms. There are many existing template matching algorithms like Sum of Absolute Difference (SAD), Normalized SAD (NSAD), Correlation methods (CORR), Normalized CORR(NCORR), Sum of Squared Difference (SSD), and Normalized SSD(NSSD). In general, as image requires more memory space for storage and much time for processing. The above said methods involves much computation. In any processing, efficiency constraints include many factors, especially accuracy of the results and speed of processing. An approach to reduce the execution time is always most appreciated. As a result of this, a novel method of partial NCC (PNCC) template matching technique is proposed in this paper. A block window approach is used to reduce the number of operations and hence to speed up the processing. A comparative study between existing NCC algorithm and the proposed partial NCC, PNCC algorithm is done. It is experimented and results proves that the execution time is reduced by 8 - 47 times approximately based on the various template images for different main images in PNCC. The accuracy of the result obtained is 100%. This proposed algorithm works for various types of images. The experiment is repeated for various sizes of templates and different sizes of main image. Further improvement in the speed of execution can be achieved by implementation of the proposed algorithm using parallel processors. It may find its importance in the real time image processing

scholarly journals Regular frequent crime pattern mining on crime datasets

2018 ◽  
Vol 7 (2.7) ◽  
pp. 972 ◽  
Author(s):  
G Vijay Kumar ◽  
M Sreedevi ◽  
G Vamsi Krishna ◽  
N Sai Ram
Keyword(s):  
Pattern Mining ◽  
The Other ◽  
Information Mining ◽  
Other Hand ◽  
Criminal Conduct ◽  
Crime Pattern

The objective of violation information mining is to comprehend different violation designs in criminal conduct in request to foresee viola-tions and expect criminal movement to stay away from the violation not to happen. Foreseeing violation is one of the worldwide difficulties looking by Law authorization office and it requires tireless endeavors with a specific end goal to limit. In this paper we are presenting anoth-er violation design called general incessant violation design which happens frequently at certain time interims utilizing vertical information arrange additionally fulfills descending conclusion property. Violation designs were not characterized by insights and its distinguishing proof is some-thing other than checking and abridging violations that are comparable in attributes and additionally area on a guide. Violation design is a gathering of at least one violations answered to or on the other hand found by the police.  

scholarly journals Mining Weighted Periodic Patterns by a Weighted Direction Graph Based Approach for Time-Series Databases

2021 ◽  
pp. 267-284
Author(s):  
Ye-In Chang ◽  
◽  
Cheng-An Fu ◽  
Jia-Zhen Que
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Data Structures ◽  
Processing Time ◽  
Pattern Mining ◽  
Periodic Pattern ◽  
Performance Study ◽  
Periodic Patterns ◽  
Memory Space ◽  
Suffix Trie

Periodic pattern mining in time series database plays an important part in data mining. However, most existing algorithms consider only the count of each item, but do not consider about the value of each item. To consider the value of each item on periodic pattern mining in time series databases, Chanda et al. proposed an algorithm called WPPM. In their algorithm, they construct the suffix trie to store the candidate pattern at first. However, the suffix trie would use too much storage space. In order to decrease the processing time for constructing the data structure, in this paper, we propose two data structures to store the candidates. The first data structure is Weighted Paired Matrix. After scanning the database, we will transform the database into the matrix type, and it is used for the second data structures. Therefore, our algorithm not only can decrease the usage of the memory space, but also the processing time. Because we do not need to use so much time to construct so many nodes and edges. Moreover, wealso consider the case of incremental mining for the increase of the data length. From the performance study, we show that our proposed algorithm based on the Weighted Direction Graphis more efficient than the WPPMalgorithm.

PROPOSAL FOR SPATIAL AND TEMPORAL COMPARISON IN THE ALGORITHMS FOR 3D RECONSTRUCTION

10.6036/9994 ◽  
2021 ◽  
Vol 96 (3) ◽  
pp. 237-237
Author(s):  
JESUS ANTONIO ALVAREZ CEDILLO ◽  
FERNANDO MARTINEZ PIÑON ◽  
TEODORO ALVAREZ SANCHEZ ◽  
JACOBO SANDOVAL GUTIERREZ ◽  
MARIO AGUILAR FERNANDEZ
Keyword(s):  
3D Reconstruction ◽  
Execution Time ◽  
Three Dimensions ◽  
Computer Reconstruction ◽  
Memory Space ◽  
3D Object ◽  
Temporal Comparison

In the computer reconstruction of objects in three dimensions (3D) there are two problems to solve. The first problem concerns reducing the memory space occupied by a 3D object. The second problem is to reduce the execution time to digitally display the reconstruction.

scholarly journals An Improved Sanitization Algorithm in Privacy-Preserving Utility Mining

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuan Liu ◽  
Genlang Chen ◽  
Shiting Wen ◽  
Guanghui Song
Keyword(s):  
Side Effects ◽  
Private Information ◽  
Pattern Mining ◽  
Privacy Preserving ◽  
Selection Strategy ◽  
Significant Information ◽  
Utility Mining ◽  
Maximum Utility ◽  
Minimum Number ◽  
Lower Ratio

High-utility pattern mining is an effective technique that extracts significant information from varied types of databases. However, the analysis of data with sensitive private information may cause privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, privacy-preserving utility mining (PPUM) has become an important research topic in recent years. The MSICF algorithm is a sanitization algorithm for PPUM. It selects the item based on the conflict count and identifies the victim transaction based on the concept of utility. Although MSICF is effective, the heuristic selection strategy can be improved to obtain a lower ratio of side effects. In our paper, we propose an improved sanitization approach named the Improved Maximum Sensitive Itemsets Conflict First Algorithm (IMSICF) to address this issue. It dynamically calculates conflict counts of sensitive items in the sanitization process. In addition, IMSICF chooses the transaction with the minimum number of nonsensitive itemsets and the maximum utility in a sensitive itemset for modification. Extensive experiments have been conducted on various datasets to evaluate the effectiveness of our proposed algorithm. The results show that IMSICF outperforms other state-of-the-art algorithms in terms of minimizing side effects on nonsensitive information. Moreover, the influence of correlation among itemsets on various sanitization algorithms’ performance is observed.

scholarly journals Optimized Incremental Mining of Customer Buying Behavior using Temporal Association Rules

2019 ◽  
Vol 8 (12) ◽  
pp. 5853-5861
Keyword(s):  
Association Rules ◽  
Execution Time ◽  
Search Space ◽  
Frequent Itemsets ◽  
Customer Behavior ◽  
Temporal Association ◽  
Incremental Mining ◽  
Buying Behavior ◽  
Memory Space ◽  
Variant Database

In retail business, customers’ behavior analytics is a study of customers’ buying behavior for a better understanding of customer needs to be able to provide service accordingly. The buying behavior is majorly influenced by the preferences of a customer. However, preferences of a customer change over a period of time due to various factors like change in income, taste, culture or newer products, etc. Understanding these changes in customer behavior is a very challenging task especially in a dynamic, ever-changing environment. There are various customer behavior mining models and techniques available in the data mining domain that are designed to work on static and dynamic databases. The traditional incremental mining techniques consider all the previous datasets in order to update the patterns. However, in a dynamic database, the size of the database grows with every update. To mine customers’ behavior in a time-variant database, the re-mining of the updated database is required that further increases processing cost in terms of execution time and memory space with every update. The purpose of this paper is to propose a method that can analyze the changes in customers’ behavior in time-variant databases without mining all the transactions. In this paper, an optimized incremental technique is proposed that utilizes temporal association rule mining in a time-variant database for mining customer behavioral patterns in an updated database. The proposed algorithm named ‘Autoregressive Moving Average model-based Incremental Temporal Association Rules Mining (ARMA-ITARM)’ utilizes the ARMA model to substantially reduce the database and maintains temporal frequent patterns in the updated database. Inspired by sliding window and pre-large concepts, the algorithm utilizes past frequent itemsets and probable frequent itemsets from customers’ purchased history along with frequent itemsets and probable frequent itemsets that reduce search space. Consequently, the entire database is scanned only once to count the frequency of occurrence of a few candidate itemsets. In effect, execution time memory need of the algorithm is very small. Experimental results demonstrate that our proposed technique performs better over recent techniques like ITARM, SWF, etc

scholarly journals Mining of High Value Item Sets with Negative Utility

2019 ◽  
Vol 8 (9) ◽  
pp. 3489-3493
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
User Preferences ◽  
Frequent Pattern ◽  
Rule Mining ◽  
Utility Mining ◽  
Real World Application ◽  
Frequent Items

The patterns generated by frequent pattern mining aims to find the frequent items without considering the utilities of the different items. The traditional association rule mining treats all items to be of equal utility. This is not always the case for a real world application. Utility based data mining is a new area of research and is complementing the frequency based approach. The main objective of Utility Mining is to identify the item sets with highest utilities, by considering profit, quantity, cost or other user preferences as the Utility of the item. Recent approaches developed so far considers the utilities of items to be same over a particular period of time. In our approach we have proposed that the utility of items vary over a period of time. Our work also proposed that the utility of items may also assume negative values. Our work thus treats the data mining in more realistic manner

scholarly journals Association Rule Mining on Spambase Dataset using Tanagra

2020 ◽  
Vol 9 (6) ◽  
pp. 890-894
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Huge Amount ◽  
Memory Space ◽  
Better Than

There is huge amount of data being generated every minute on internet. This data is of no use until we cannot extract useful information from it. Data mining is the process of extracting useful information or knowledge from this huge amount of data that can be further used for various purposes. Discovering Association rules is one of the most important tasks among all other data mining tasks. Association rules contain the rules in the form of IF then THAN form. The leftmost part of the rule i.e. IF is called as the Antecedent which defines the condition and the rightmost part i.e. ELSE is called as the Consequent which defines the result. In this paper, we present the overview and comparison of Apriori, Apriori PT and Frequent Itemsets algorithm of association component in Tanagra Tool. We analyzed the performance based on the execution time and memory used for different number of instances, support and Rule Length in Spambase Dataset. The results show that when we increase the support value the Apriori PT takes the less execution time and Apriori takes less memory space. When numbers of instances are reduced Frequent Itemsets outperforms well both in case of memory and execution time. When rule length is increased the Apriori algorithm performs better than Apriori PT and Frequent Itemsets.

scholarly journals Classification with Single Constraint Progressive Mining of Sequential Patterns

2017 ◽  
Vol 7 (4) ◽  
pp. 2142
Author(s):  
Regina Yulia Yasmin ◽  
Putri Saptawati ◽  
Benhard Sitohang
Keyword(s):  
Execution Time ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Classification Task ◽  
Sequential Patterns ◽  
Single Constraint ◽  
Long Time ◽  
The Right

<span>Classification based on sequential pattern data has become an important topic to explore. One of research has been carried was the Classify-By-Sequence, CBS. CBS classified data based on sequential patterns obtained from AprioriLike sequential pattern mining. Sequential patterns obtained were called CSP, Classifiable Sequential Patterns. CSP was used as classifier rules or features for the classification task. CBS used AprioriLike algorithm to search for sequential patterns. However, AprioriLike algorithm took a long time to search for them. Moreover, not all sequential patterns were important for the user. In order to get the right and meaningful features for classification, user uses a constraint in sequential pattern mining. Constraint is also expected to reduce the number of sequential patterns that are short and less meaningful to the user. Therefore, we developed CBS_CLASS* with Single Constraint Progressive Mining of Sequential Patterns or Single Constraint PISA or PISA*. CBS_Class* with PISA* was proven to classify data in faster time since it only processed lesser number of sequential patterns but still conform to user’s need. The experiment result showed that compared to CBS_CLASS, CBS_Class* reduced the classification execution time by 89.8%. Moreover, the accuracy of the classification process can still be maintained.</span><p> </p>

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