scholarly journals A New Length-Based Algebraic Multigrid Clustering Algorithm

VLSI Design ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
L. Rakai ◽  
A. Farshidi ◽  
L. Behjat ◽  
D. Westwick
Keyword(s):  
Clustering Algorithm ◽  
A Priori ◽  
Clustering Algorithms ◽  
Algebraic Multigrid ◽  
Estimation Technique ◽  
Wire Length ◽  
Clustering Technique ◽  
Length Estimation ◽  
Made In

Clustering algorithms have been used to improve the speed and quality of placement. Traditionally, clustering focuses on the local connections between cells. In this paper, a new clustering algorithm that is based on the estimated lengths of circuit interconnects and the connectivity is proposed. In the proposed algorithm, first an a priori length estimation technique is used to estimate the lengths of nets. Then, the estimated lengths are used in a clustering framework to modify a clustering technique based on algebraic multigrid (AMG), that finds the cells with the highest connectivity. Finally, based on the results from the AMG-based process, clusters are made. In addition, a new physical unclustering technique is proposed. The results show a significant improvement, reductions of up to 40%, in wire length can be achieved when using the proposed technique with three academic placers on industry-based circuits. Moreover, the runtime is not significantly degraded and can even be improved.

scholarly journals A SELF-ORGANIZING MAP FOR MIXED CONTINUOUS AND CATEGORICAL DATA

2011 ◽  
pp. 24-32 ◽  
Author(s):  
Nicoleta Rogovschi ◽  
Mustapha Lebbah ◽  
Younès Bennani
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mixed Data ◽  
Categorical Variables ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Public Data ◽  
Self Organizing

Most traditional clustering algorithms are limited to handle data sets that contain either continuous or categorical variables. However data sets with mixed types of variables are commonly used in data mining field. In this paper we introduce a weighted self-organizing map for clustering, analysis and visualization mixed data (continuous/binary). The learning of weights and prototypes is done in a simultaneous manner assuring an optimized data clustering. More variables has a high weight, more the clustering algorithm will take into account the informations transmitted by these variables. The learning of these topological maps is combined with a weighting process of different variables by computing weights which influence the quality of clustering. We illustrate the power of this method with data sets taken from a public data set repository: a handwritten digit data set, Zoo data set and other three mixed data sets. The results show a good quality of the topological ordering and homogenous clustering.

Customer Behavior Prediction using K-Means Clustering Algorithm

2016 ◽  
pp. 256-267 ◽  
Author(s):  
Juhi Singh ◽  
Mandeep Mittal ◽  
Sarla Pareek
Keyword(s):  
Association Rules ◽  
Clustering Algorithm ◽  
A Priori ◽  
Clustering Algorithms ◽  
Customer Behavior ◽  
Purchase Behavior ◽  
Behavior Prediction ◽  
Customer Data ◽  
Customers Segmentation ◽  
Insight Into

Due to the increased availability of individual customer data, it is possible to predict customer buying pattern. Customers can be segmented using clustering algorithms based on various parameters such as Frequency, Recency and Monetary values (RFM). The data can further be analyzed to infer rules among two or more purchases of the customer. In this chapter we will present a clustering algorithm, enhanced k- means algorithm, which is based on k- means algorithm to divide customers into various segments. After segmentation, each segment is mined with the help of a priori algorithm to infer rules so that the customer's purchase behavior can be predicted. From large number of association rules with sufficient coverage, the customer's purchasing pattern can be predicted. Experiment on real database is implemented to evaluate the performance on effectiveness and utility of the approach. The results show that the proposed approach can gain a well insight into customers' segmentation and thus their behavior can be predicted.

Clustering Algorithms for Direct Current Track Coded Signals

2019 ◽  
Author(s):  
Song Qin ◽  
Nenad Mijatovic ◽  
Jeffrey Fries ◽  
James Kiss
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
High Availability ◽  
Digital Analysis ◽  
Signaling Systems ◽  
Track Circuits ◽  
Service Conditions ◽  
Track Circuit ◽  
Fail Safe

Designed for detecting train presence on tracks, track circuits must maintain a level of high availability for railway signaling systems. Due to the fail-safe nature of these critical devices, any failures will result in a declaration of occupancy in a section of track which restricts train movements. It is possible to automatically diagnose and, in some cases, predict the failures of track circuits by performing analytics on the track signals. In order to perform these analytics, we need to study the coded signals transmitted to and received from the track. However, these signals consist of heterogeneous pulses that are noisy for data analysis. Thus, we need techniques which will automatically group homogeneous pulses into similar groups. In this paper, we present data cleansing techniques which will cluster pulses based on digital analysis and machine learning. We report the results of our evaluation of clustering algorithms that improve the quality of analytic data. The data were captured under revenue service conditions operated by Alstom. For clustering algorithm, we used the k-means algorithm to cluster heterogeneous pulses. By tailoring the parameters for this algorithm, we can control the pulses of the cluster, allowing for further analysis of the track circuit signals in order to gain insight regarding its performance.

scholarly journals Fine-Tuning an Algorithm for Semantic Document Clustering Using a Similarity Graph

2016 ◽  
Vol 10 (04) ◽  
pp. 527-555
Author(s):  
Lubomir Stanchev
Keyword(s):  
English Language ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Fine Tuning ◽  
Human Judgment ◽  
Multiple Parameters ◽  
Similarity Graph ◽  
Multiple Metrics

In this article, we examine an algorithm for document clustering using a similarity graph. The graph stores words and common phrases from the English language as nodes and it can be used to compute the degree of semantic similarity between any two phrases. One application of the similarity graph is semantic document clustering, that is, grouping documents based on the meaning of the words in them. Since our algorithm for semantic document clustering relies on multiple parameters, we examine how fine-tuning these values affects the quality of the result. Specifically, we use the Reuters-21578 benchmark, which contains [Formula: see text] newswire stories that are grouped in 82 categories using human judgment. We apply the k-means clustering algorithm to group the documents using a similarity metric that is based on keywords matching and one that uses the similarity graph. We evaluate the results of the clustering algorithms using multiple metrics, such as precision, recall, f-score, entropy, and purity.

A new a priori net length estimation technique for integrated circuits using radial basis functions

2013 ◽  
Vol 39 (4) ◽  
pp. 1204-1218 ◽  
Author(s):  
Amin Farshidi ◽  
Logan Rakai ◽  
Bardia Samimi ◽  
Laleh Behjat ◽  
David Westwick
Keyword(s):  
Integrated Circuits ◽  
Radial Basis Functions ◽  
A Priori ◽  
Basis Functions ◽  
Estimation Technique ◽  
Length Estimation ◽  
Radial Basis

Algorithms Optimization for Intelligent IoV Applications

2021 ◽  
pp. 1-25
Author(s):  
Elmustafa Sayed Ali Ahmed ◽  
Zahraa Tagelsir Mohammed ◽  
Mona Bakri Hassan ◽  
Rashid A. Saeed
Keyword(s):  
Quality Of Service ◽  
Internet Of Things ◽  
Network Topology ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Topology Changes ◽  
The Internet Of Things

Internet of vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. It is a part of the internet of things (IoT) which deals with vehicle communications. As vehicular nodes are considered always in motion, they cause frequent changes in the network topology. These changes cause issues in IoV such as scalability, dynamic topology changes, and shortest path for routing. In this chapter, the authors will discuss different optimization algorithms (i.e., clustering algorithms, ant colony optimization, best interface selection [BIS] algorithm, mobility adaptive density connected clustering algorithm, meta-heuristics algorithms, and quality of service [QoS]-based optimization). These algorithms provide an important intelligent role to optimize the operation of IoV networks and promise to develop new intelligent IoV applications.

Applications of a Priori Wire Length Estimation

2001 ◽  
pp. 233-242
Author(s):  
Dirk Stroobandt
Keyword(s):  
A Priori ◽  
Wire Length ◽  
Length Estimation

A Priori Wire Length Estimation

2001 ◽  
pp. 131-194
Author(s):  
Dirk Stroobandt
Keyword(s):  
A Priori ◽  
Wire Length ◽  
Length Estimation

MR-BIRCH: A scalable MapReduce-based birch clustering algorithm

2020 ◽  
pp. 1-11
Author(s):  
Yufeng Li ◽  
HaiTian Jiang ◽  
Jiyong Lu ◽  
Xiaozhong Li ◽  
Zhiwei Sun ◽  
...  
Keyword(s):  
Big Data ◽  
Real World ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Statistical Information ◽  
Main Memory ◽  
Acceptable Result ◽  
Clustering Quality ◽  
Synthetic Datasets

Many classical clustering algorithms have been fitted into MapReduce, which provides a novel solution for clustering big data. However, several iterations are required to reach an acceptable result in most of the algorithms. For each iteration, a new MapReduce job must be executed to load the dataset into main memory, which results in high I/O overhead and poor efficiency. BIRCH algorithm stores only the statistical information of objects with CF entries and CF tree to cluster big data, but with the increase of the tree nodes, the main memory will be insufficient to contain more objects. Hence, BIRCH has to reduce the tree, which will degrade the clustering quality and decelerate the whole execution efficiency. To deal with the problem, BIRCH was fitted into MapReduce called MR-BIRCH in this paper. In contrast to a great number of MapReduce-based algorithms, MR-BIRCH loads dataset only once, and the dataset is processed parallel in several machines. The complexity and scalability were analyzed to evaluate the quality of MR-BIRCH, and MR-BIRCH was compared with Python sklearn BIRCH and Apache Mahout k-means on real-world and synthetic datasets. Experimental results show, most of the time, MR-BIRCH was better or equal to sklearn BIRCH, and it was competitive to Mahout k-means.

An improved ant algorithm with LDA-based representation for text document clustering

2016 ◽  
Vol 43 (2) ◽  
pp. 275-292 ◽  
Author(s):  
Aytug Onan ◽  
Hasan Bulut ◽  
Serdar Korukoglu
Keyword(s):  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Clustering Methods ◽  
Initial Value ◽  
Text Document ◽  
Clustering Quality ◽  
Text Features

Document clustering can be applied in document organisation and browsing, document summarisation and classification. The identification of an appropriate representation for textual documents is extremely important for the performance of clustering or classification algorithms. Textual documents suffer from the high dimensionality and irrelevancy of text features. Besides, conventional clustering algorithms suffer from several shortcomings, such as slow convergence and sensitivity to the initial value. To tackle the problems of conventional clustering algorithms, metaheuristic algorithms are frequently applied to clustering. In this paper, an improved ant clustering algorithm is presented, where two novel heuristic methods are proposed to enhance the clustering quality of ant-based clustering. In addition, the latent Dirichlet allocation (LDA) is used to represent textual documents in a compact and efficient way. The clustering quality of the proposed ant clustering algorithm is compared to the conventional clustering algorithms using 25 text benchmarks in terms of F-measure values. The experimental results indicate that the proposed clustering scheme outperforms the compared conventional and metaheuristic clustering methods for textual documents.

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