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Zhang, Tian; Ramakrishnan, Raghu; Livny, Miron

doi:10.1023/a:1009783824328

Cited by 716 publications

(65 citation statements)

References 21 publications

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“…Among these are the K-mode (Huang 1998), BIRCH algorithm (Zhang et al 1997), the Hierarchical Clustering, and the two-step clustering approach developed by SPSS (SPSS 2007). Two-step clustering is a system well-suited for identifying an adequate number of clusters and coping with multi-attributed and multi-distributed datasets while not being sensitive for distribution of variables (Bacher et al 2004).…”

Section: Methodsmentioning

confidence: 99%

Opportunities and challenges for smallholder pig production systems in a mountainous region of Xishuangbanna, Yunnan Province, China

Riedel

Schiborra

Huelsebusch

et al. 2012

Trop Anim Health Prod

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China’s small-scale pig keepers are the largest community of pork producers worldwide. About 56 % of the world's pigs originate from such systems, each producing 2–5 head per year. This study analyzes pig smallholders in Xishuangbanna, a prefecture of Yunnan Province. Categorical principal component analysis and two-step cluster analysis were used to identify three main production systems: livestock-corn-based (LB; 41 %), rubber based (RB; 39 %), and pig based (PB; 20 %) systems. RB farms earn high income from rubber and fatten cross-bred pigs, often using purchased feeds. PB farms own similar-sized rubber plantations and raise pigs, with fodder mainly being cultivated and collected in the forest. LB farms grow corn, rice, and tea while also raising pigs, fed with collected and cultivated fodder as well. About one third of pigs were marketed (LB, 20 %; RB, 42 %; PB, 25 %), and local pig meat is highly appreciated in the nearby town. High mortality, low reproductive performance, and widespread malnourishment are the systems' main constraints. Basic training in hygiene and reproduction management could significantly increase production; most effective measures would be counterbalancing seasonal malnourishment and exploration of locally available protein feeds. Through support by external expertise, farmers could more effectively trade their pigs at lucrative town markets.

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Section: Methodsmentioning

confidence: 99%

Opportunities and challenges for smallholder pig production systems in a mountainous region of Xishuangbanna, Yunnan Province, China

Riedel

Schiborra

Huelsebusch

et al. 2012

Trop Anim Health Prod

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“…Different improvements to alleviate effects of initialization on results of KMeans are proposed [4,34]. While we are not concerned here with scalability issues, we note that classic K-Means has numerous extensions to scalable unsupervised learning in databases CLARANS [14] and BIRCH [35].…”

Section: Related Workmentioning

confidence: 99%

Learning Simple Relations: Theory and Applications

Berkhin¹,

Becher²

2002

Proceedings of the 2002 SIAM International Conference on Data Mining

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-In addition to classic clustering algorithms, many different approaches to clustering are emerging for objects of special nature. In this article we deal with the grouping of rows and columns of a matrix with non-negative entries. Two rows (or columns) are considered similar if corresponding cross-distributions are close. This grouping is a dual clustering of two sets of elements, row and column indices. The introduced approach is based on the minimization of reduction of mutual information contained in a matrix that represents the relationship between two sets of elements. Our clustering approach contains many parallels with K-Means clustering due to certain common algebraic properties. The obtained results have many applications, including grouping of Web visit data.

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“…A description of the little but promising work already been undertaken in this direction can be found in (Azzag et al, 2006). But a more extensive and systematic research effort is necessary to make the ant based hierarchical models superior to existing algorithms like Birch (Zhang et al, 1997).…”

Section: Discussionmentioning

confidence: 99%

Swarm Intelligence Algorithms for Data Clustering

Abraham

Das

Roy³

2008

Soft Computing for Knowledge Discovery and Data Mining

125

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Summary. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This chapter explores the role of SI in clustering different kinds of datasets. It finally describes a new SI technique for partitioning any dataset into an optimal number of groups through one run of optimization. Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm.

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Untitled

Cited by 716 publications

References 21 publications

Opportunities and challenges for smallholder pig production systems in a mountainous region of Xishuangbanna, Yunnan Province, China

Opportunities and challenges for smallholder pig production systems in a mountainous region of Xishuangbanna, Yunnan Province, China

Learning Simple Relations: Theory and Applications

Swarm Intelligence Algorithms for Data Clustering

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