scholarly journals Decorative Art Pattern Mining and Discovery based on Group User Intelligence

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Pattern Mining ◽  
Discovery Process ◽  
Decorative Art ◽  
Clustering Approach ◽  
Effectiveness And Efficiency ◽  
Discovery Method ◽  
The Web ◽  
Real Estates

With the continuous developments of real estates and the increasing personalization of people, more and more house owners are willing to search for and discover their preferred decorative art patterns via various house decoration cases sharing websites or platforms. Through browsing and analyzing existing house decoration cases on the Web, a new house owner can find out his or her interested decorative art patterns; however, the above decorative art pattern mining and discovery process is often time-consuming and boring due to the big volume of existing house decoration cases on the Web. Therefore, it is becoming a challenging task to develop a time-efficient decorative art pattern mining and discovery method based on the available house decoration cases provided by historical users. Considering this challenge, a novel LSH-based similar house owners clustering approach is proposed. A set of experiments are designed to validate the effectiveness and efficiency of our proposal.

scholarly journals Decorative Art Pattern Mining and Discovery Based on Group User Intelligence

2021 ◽  
Vol 33 (6) ◽  
pp. 1-12
Author(s):  
Kangning Shen ◽  
Rongrong Tu ◽  
Rongju Yao ◽  
Sifeng Wang ◽  
Ashish K. Luhach
Keyword(s):  
Pattern Mining ◽  
Discovery Process ◽  
Decorative Art ◽  
Clustering Approach ◽  
Effectiveness And Efficiency ◽  
Discovery Method ◽  
The Web ◽  
Real Estates

With the continuous developments of real estates and the increasing personalization of people, more and more house owners are willing to search for and discover their preferred decorative art patterns via various house decoration cases sharing websites or platforms. Through browsing and analyzing existing house decoration cases on the Web, a new house owner can find out his or her interested decorative art patterns; however, the above decorative art pattern mining and discovery process is often time-consuming and boring due to the big volume of existing house decoration cases on the Web. Therefore, it is becoming a challenging task to develop a time-efficient decorative art pattern mining and discovery method based on the available house decoration cases provided by historical users. Considering this challenge, a novel LSH-based similar house owners clustering approach is proposed. A set of experiments are designed to validate the effectiveness and efficiency of our proposal.

Harnessing Collective Intelligence Through Pattern Mining in Social Computational Systems

2018 ◽  
pp. 91-110
Author(s):  
Gaganmeet Kaur Awal ◽  
K. K. Bharadwaj
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Pattern Mining ◽  
Collective Intelligence ◽  
Social Systems ◽  
Big Data Analytics ◽  
Diverse Range ◽  
Business Applications ◽  
Computational Systems ◽  
The Web

Due to the digital nature of the web, the social web mimics the real-world social dynamics that manifest themselves as data and can be easily mined as patterns, making the web a fertile ground for business and research-oriented analytical applications. Collective intelligence (CI) is a multifaceted field with roots in sociology, biology, and many other disciplines. Various manifestations of CI support the successful existence of large-scale social systems. This chapter gives an overview of the principles of CI and the concept of “wisdom of crowds” and highlights how to maximize the potential of big data analytics for CI. Also, various techniques and approaches have been described that leverage these CI concepts across a diverse range of ever-evolving social systems for commercial business applications like influence mining, expertise discovery, etc.

Traversal Pattern Mining in Web Usage Data

2008 ◽  
pp. 2004-2021
Author(s):  
Jenq-Foung Yao ◽  
Yongqiao Xiao
Keyword(s):  
Pattern Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Web Usage ◽  
Web Logs ◽  
Frequent Episodes ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are also discussed.

A Constraint Programming Approach for Web Log Mining

2016 ◽  
Vol 11 (4) ◽  
pp. 24-42 ◽  
Author(s):  
Amina Kemmar ◽  
Yahia Lebbah ◽  
Samir Loudni
Keyword(s):  
Constraint Programming ◽  
Pattern Mining ◽  
Programming Approach ◽  
Web Log Mining ◽  
Web Log ◽  
Web Access ◽  
Log Mining ◽  
Log File ◽  
Access Patterns ◽  
The Web

Mining web access patterns consists in extracting knowledge from server log files. This problem is represented as a sequential pattern mining problem (SPM) which allows to extract patterns which are sequences of accesses that occur frequently in the web log file. There are in the literature many efficient algorithms to solve SMP (e.g., GSP, SPADE, PrefixSpan, WAP-tree, LAPIN, PLWAP). Despite the effectiveness of these methods, they do not allow to express and to handle new constraints defined on patterns, new implementations are required. Recently, many approaches based on constraint programming (CP) was proposed to solve SPM in a declarative and generic way. Since no CP-based approach was applied for mining web access patterns, the authors introduce in this paper an efficient CP-based approach for solving the web log mining problem. They bring back the problem of web log mining to SPM within a CP environment which enables to handle various constraints. Experimental results on non-trivial web log mining problems show the effectiveness of the authors' CP-based mining approach.

scholarly journals A Grid-Density Based Algorithm by Weighted Spiking Neural P Systems with Anti-Spikes and Astrocytes in Spatial Cluster Analysis

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1132
Author(s):  
Deting Kong ◽  
Yuan Wang ◽  
Xinyan Wu ◽  
Xiyu Liu ◽  
Jianhua Qu ◽  
...  
Keyword(s):  
Dimensional Space ◽  
P Systems ◽  
High Dimensional ◽  
P System ◽  
Inhibitory Influence ◽  
Spiking Neural P Systems ◽  
Clustering Approach ◽  
Spatial Cluster Analysis ◽  
Effectiveness And Efficiency ◽  
Real World Datasets

In this paper, we propose a novel clustering approach based on P systems and grid- density strategy. We present grid-density based approach for clustering high dimensional data, which first projects the data patterns on a two-dimensional space to overcome the curse of dimensionality problem. Then, through meshing the plane with grid lines and deleting sparse grids, clusters are found out. In particular, we present weighted spiking neural P systems with anti-spikes and astrocyte (WSNPA2 in short) to implement grid-density based approach in parallel. Each neuron in weighted SN P system contains a spike, which can be expressed by a computable real number. Spikes and anti-spikes are inspired by neurons communicating through excitatory and inhibitory impulses. Astrocytes have excitatory and inhibitory influence on synapses. Experimental results on multiple real-world datasets demonstrate the effectiveness and efficiency of our approach.

Effectiveness and efficiency: the need for tailorable user interfaces on the Web

1998 ◽  
Vol 30 (1-7) ◽  
pp. 499-508 ◽  
Author(s):  
Wolfgang Appelt ◽  
Elke Hinrichs ◽  
Gerd Woetzel
Keyword(s):  
User Interfaces ◽  
Effectiveness And Efficiency ◽  
The Web

Sequential Pattern Mining from Sequential Data

2009 ◽  
pp. 622-631
Author(s):  
Shigeaki Sakurai
Keyword(s):  
Pattern Mining ◽  
Pattern Discovery ◽  
Sequential Pattern ◽  
The Other ◽  
Sequential Patterns ◽  
Sequential Data ◽  
Frequent Patterns ◽  
New Knowledge ◽  
Discovery Method ◽  
Time Information

Owing to the progress of computer and network environments, it is easy to collect data with time information such as daily business reports, weblog data, and physiological information. This is the context in which methods of analyzing data with time information have been studied. This chapter focuses on a sequential pattern discovery method from discrete sequential data. The methods proposed by Pei et al. (2001), Srikant & Agrawal (1996), and Zaki (2001) efficiently discover the frequent patterns as characteristic patterns. However, the discovered patterns do not always correspond to the interests of analysts, because the patterns are common and are not a source of new knowledge for the analysts. The problem has been pointed out in connection with the discovery of associative rules. Blanchard et al. (2005), Brin et al. (1997), Silberschatz et al. (1996), and Suzuki et al. (2005) propose other criteria in order to discover other kinds of characteristic patterns. The patterns discovered by the criteria are not always frequent but are characteristic of viewpoints. The criteria may be applicable to discovery methods of sequential patterns. However, these criteria do not satisfy the Apriori property. It is difficult for the methods based on the criteria to efficiently discover the patterns. On the other hand, methods that use the background knowledge of analysts have been proposed in order to discover sequential patterns corresponding to the interests of analysts (Garofalakis et al., 1999; Pei et al., 2002; Sakurai et al., 2008b; Yen, 2005).

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