Clustering is the process of locating patterns in large data sets. As databases continue to grow in size, efficient and effective clustering algorithms play a paramount role in data mining applications. Traditional clustering approaches usually analyze static datasets in which objects are kept unchanged after being processed, but many practical datasets are dynamically modified which means some previously learned patterns have to be updated accordingly. Re-clustering the whole dataset from scratch is not a good choice due to the frequent data modifications and the limited out-of-service time, so the development of incremental clustering approaches is highly desirable. In this paper, we propose an incremental algorithm, IPYRAMID: Incremental Parallel hYbrid clusteRing using genetic progrAmming and Multiobjective fItness with Density employs a combination of data parallelism, genetic programming (GP), special operators, and multi-objective density-based incremental fitness function. Although many incremental clustering algorithms have been proposed which can handle insertion of new record properly using incremental approach but cannot handle deletion of record properly. This issue is resolved in the proposed algorithm and density based incremental fitness function that helps to handle outliers. Use of parallelism increases the speed of execution as well as identifies clusters of arbitrary shapes. The incremental merge engine can dynamically determine the number of clusters. Preliminary experimental results show that it can increase the efficiency of clustering process.

/pdf/incremental-cluster-detection-using-a-soft-computing-evox7r0725.pdf

Incremental Cluster Detection using a Soft Computing Approach

The emerging technologies and data centric applications have been becoming an integral part of business intelligence, decision process and numerous daily activities. To enable efficient pattern classification and data analysis, clustering has emerged as a potential mechanism that classifies data elements based on respective feature homogeneity. Although K-Means clustering has exhibited appreciable performance for data clustering, it suffers to enable optimal classification with high dimensional data sets. Numerous optimization efforts including genetic algorithm (GA) based clustering also require further optimization to avoid local minima issues. In this paper, an improved Canonical GA based Bisecting K-Means algorithm (CGABC) has been developed. The proposed model incorporates min-max normalization based feature normalization of the high dimensional data sets, which is followed by T-Test analysis that significantly reduces data dimensions based on feature similarity of the data elements. The fitness value has been assigned based on inter-cluster (heterogeneous distance) and within-cluster (homogeneous distance) distances. To enable optimal features and process parameter selection, particularly cluster centers information, the conventional GA has been modified by applying multistage reproduction process, enhanced crossover and mutation. By incorporating the optimized cluster center information the Bisecting K-Means clustering has been performed, which has exhibited optimal solution for highly accurate and efficient clustering with high dimensional data sets.

Evolutionary computing based hybrid bisecting clustering algorithm for multidimensional data

Pattern classification and data clustering has emerged as a potential mechanism that classifies data elements based on respective feature homogeneity. Although K-Means clustering has exhibited appreciable performance for data clustering, it suffers from poor optimality when dealing with multi dimensional datasets. In this paper, an evolutionary programming based Bisecting K-Means algorithm called Canonical Genetic Algorithm based Bisecting Clustering (CGABC) has been developed. The proposed model incorporates min-max normalization based feature normalization of the high dimensional data sets, which is followed by T-Test analysis. The fitness value has been assigned based on inter-cluster (heterogeneous distance) and within-cluster (homogeneous distance) distances. The conventional GA has been modified by applying multistage reproduction process, enhanced crossover and mutation. By incorporating the optimized cluster center information, the Bisecting K-Means clustering has been performed, which has exhibited optimal solution for highly accurate and efficient clustering with high dimensional data sets.

A pragmatic approach for multidimensional data clustering

Clustering is an important, hard and active topic in data analysis and pattern recognition. K clustering is a branch of data clustering where the number of clusters is know in advance. Recently, spectral clustering (SC) becomes one of the most popular and appealing k clustering methods because of its generality, efficiency and its rich theoretical foundation. But the final results obtained from SCs depend on spectral relaxation which may have no guarantee on the quality of the solution. In order to overcome the SCs’ shortcoming, we propose an effective GAKC algorithm by using a genetic algorithm to search for the optimal cluster result of SCs. The algorithm uses group number coding chromosome, a new uniform crossover operator and exponential mutation rate. To verify the effectiveness of GAKC, a comparison among the experimental results of the proposed GAKC, a classical GA-based method by Ujjwal Malulik and the SC methods by SM and NJW on a real-life data set is presented. The conclusion comes that the proposed algorithm can gain much more accurate clustering result.

GAKC: A New GA-Based k Clustering Algorithm

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Luo Min

Author Tools

Chat about Author

Papers

GAKC: A New GA-Based k Clustering Algorithm