A weighted kernel possibilistic <i>c</i>‐means algorithm based on cloud computing for clustering big data

Zhang, Qingchen; Chen, Zhikui

doi:10.1002/dac.2844

Cited by 48 publications

(6 citation statements)

References 37 publications

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“…The diverse research papers using the PCM bunching for the large information are intricately talked about underneath, Qingchen Zhang and Zhikui Chen [12] recommended a weighted piece PCM calculation (wkPCM) for grouping the information objects into the reasonable gatherings. The part loads were coordinated for characterizing the item's significance in the bit grouping for limiting the defilement created by the uproarious information.…”

Section: Possibilistic C-implies (Pcm) Groupingmentioning

confidence: 99%

“…Clustering is in a class of unsupervised learning techniques, unlike classification, in which similar objects of the dataset are grouped into clusters [9] , and thus form different clusters such that objects in the same cluster groups are very different from each other and objects in the same group or cluster are very similar to each other [10,11] . The clusters are known only after the complete execution of the clustering algorithm [12] . Two clustering algorithms that are used for managing large datasets are Density Based and Fuzzy clustering algorithm each of which is summarized below.…”

Section: Clustering Analysis Techniquesmentioning

confidence: 99%

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Density based clustering and fuzzy clustering for efficient clustering of big data in hadoop ecosystem

Prasanna¹

2020

Int. J. Eng. Comput. Sci.

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In this paper suggesting to use parallel distributed Hadoop Map Reduce technology. Map Reduce is a parallel technology which can process bigdata by creating multiple instances of thread. Map will take input data and split it into multiple chunks or parts and distribute all parts to different reducers. All reducers will process the data and send result back to mapper. Mapper gather output from all mappers and then generate a single output. Due to multiple parallel processing of Map Reduce technology allow us to process any amount of data. In this paper author describing clustering algorithms such Density Based Clustering and Fuzzy Clustering. Both algorithms are not efficient to group all similar data to single cluster and make some data to compromise by putting little un-similar to different clusters. To implement this project author is using National Climatic Data Center (NCDC) dataset which contains climate information. To find out similar temperature on different dates author is applying Hybrid clustering algorithm. From this dataset author is using date and temperature value and then passing date as key to Mapper and temperature as value to Mapper. Mapper always read data in the form of key value pairs. In dataset date we can find at position 6 to14 and temperature we can find at position 39 to 45. Below are some dataset example values.

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Section: Possibilistic C-implies (Pcm) Groupingmentioning

confidence: 99%

Section: Clustering Analysis Techniquesmentioning

confidence: 99%

Density based clustering and fuzzy clustering for efficient clustering of big data in hadoop ecosystem

Prasanna¹

2020

Int. J. Eng. Comput. Sci.

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“…Cloud computing offers a scalable and cost-efficient solution for big data clustering by providing tremendous memory space and high processing power [18]. Although, some improved PCM algorithms have been proposed for big data clustering, such as weighted kernel possibilistic c-means [19], incremental WPCM [20,21], distributed weighted possibilistic c-means [22] and secure weighted possibilistic c-means for privacy-preserving big data clustering on the cloud [23]. These algorithms suffer from several problems, they are based on approximating the required calculations using Taylor's expansion and always produce lower clustering accuracy than their corresponding conventional algorithms.…”

Section: Introductionmentioning

confidence: 99%

PPS-Fpcm: Privacy-Preserving Semi-Fuzzy Possibilistic C-Means

Mahfouz

2023

ijarcs

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Applying traditional clustering techniques to big data on the cloud while preserving the privacy of the data is a challenge due to the required division and exponential operations in each iteration, which complicate its implementation on encrypted data. Several existing approaches are based on approximating the formulas of centers, weights, and memberships as three polynomial functions according to the multivariate Taylor formula. However, they usually suffer an increase in complexity and a slight drop in accuracy. In this paper, a novel Privacy-Preserving semi-fuzzy clustering algorithm based on the possibilistic paradigm, termed PPS-FPCM, is presented. Its main feature is that it avoids exponentiation and division operations, at each iteration, without losing accuracy. By restricting the typicality to an ordered set of discrete values between zero and one decided by the data owner (DO), the computation is simplified. The second key idea is the use of this soft typicality to detect outliers and compute the corresponding semi-fuzzy memberships, which is used to increase the in-between cluster distance. However, the initial typicality requires a magnitude relation comparison, which is still difficult to do over encrypted data. In this research study, we show how the existing incomplete re-encryption method can be used to tackle this problem. In each iteration, centers and distances to the new centers are computed on a calculator cloud server (CaCS) which is responsible for storing the cipher texts of the (DO)’s data and processing them. Then, CaCS sends the incompletely re-encrypted difference between these distances and iteratively updated bin values that correspond to the discrete possibilistic memberships that are initially decided by the (DO) to the comparator cloud server (CoCS). CoCS decrypts the difference and returns the results of comparisons. When CaCS receives the results of comparison from CoCS, it decides on an appropriate soft typicality or resends the difference of the same distance to another bin value. The required number of comparisons is O(log the number of bins). CaCS iteratively computes the corresponding semi-fuzzy memberships, computes the refined memberships, and updates the centers. In the end, CaCS sends the final soft memberships and centers to the (DO). The proposed algorithm is applicable to normal data and homomorphically encrypted data, is more effective than several related algorithms, and can produce accurate results using large enough (16 or more) discrete values with a high reduction on runtime as the number of comparisons is much less complex than exponential and division operations with added communication cost between CaCS and CoCS.

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“…It has the advantages of low cost, large storage space, fast processing speed and so on. All the information related research in the world has started to rely on it (Zhang & Chen, 2016). In the construction of the accounting information system, the cloud computing technology is used to obtain, cluster and analyze large accounting data, which not only overcomes the problem of large cost of traditional accounting information mode, but also greatly improves the efficiency of analyzing massive accounting data, and gradually exerts the value of accounting big data analysis.…”

Section: Introductionmentioning

confidence: 99%

Research on the Construction of Enterprise Accounting Data Analysis Platform Based on Cloud Computing

Zhang¹

2022

OJBM

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With the emergence of massive data inside and outside the enterprise, paying much attention to the processing and analysis of accounting big data can bring huge value-added value to enterprises, and adapt to complex and changing economic environment. Based on this, by analyzing the related theories of accounting big data, accounting information and cloud computing, we build a cloud computing storage module of big data analysis platform, and apply Apriori data mining algorithm based on association rules to deal with massive data of accounting. A comparative prediction of the financial status of a group shows that the maximum error is less than 8% compared with the actual results, thus verifying the reliability and superiority of the established accounting big data analysis platform based on cloud computing.

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A weighted kernel possibilistic c‐means algorithm based on cloud computing for clustering big data

Cited by 48 publications

References 37 publications

Density based clustering and fuzzy clustering for efficient clustering of big data in hadoop ecosystem

Density based clustering and fuzzy clustering for efficient clustering of big data in hadoop ecosystem

PPS-Fpcm: Privacy-Preserving Semi-Fuzzy Possibilistic C-Means

Research on the Construction of Enterprise Accounting Data Analysis Platform Based on Cloud Computing

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