Cluster Analysis with Balancing Weight on Mixed-type Data
نویسندگان
چکیده
منابع مشابه
Cluster Analysis with Balancing Weight on Mixed-type Data1)
A set of clustering algorithms with proper weight on the formulation of distance which extend to mixed numeric and multiple binary values is presented. A simple matching and Jaccard coefficients are used to measure similarity between objects for multiple binary attributes. Similarities are converted to dissimilarities between th and th objects. The performance of clustering algorithms with b...
متن کامل‘BALANCING AND SEQUENCING’ VERSUS ‘ONLY BALANCING’ IN MIXED MODEL U-LINE ASSEMBLY SYSTEMS: AN ECONOMIC ANALYSIS
With the growth in customers’ demand diversification, mixed-model U-lines (MMUL) have acquired increasing importance in the area of assembly systems. There are generally two different approaches in the literature for balancing such systems. Some researchers believe that since the types of models can be very diverse, a balancing approach without simultaneously sequencing of models will not yield...
متن کاملMixture model of Gaussian copulas to cluster mixed-type data
A mixture model of Gaussian copulas is proposed to cluster mixed data. This approach allows to straightforwardly define simple multivariate intra-class dependency models while preserving classical distributions for the one-dimensional margins of each component in order to facilitate the model interpretation. Moreover, the intra-class dependencies are taken into account by the Gaussian copulas w...
متن کاملMixed-Model Assembly Line Balancing with Considering Reliability
This paper presents a multi-objective simulated annealing algorithm for the mixed-model assembly line balancing with stochastic processing times. Since, the stochastic task times may have effects on the bottlenecks of a system, maximizing the weighted line efficiency (equivalent to the minimizing the number of station), minimizing the weighted smoothness index and maximizing the system reliabil...
متن کاملmodeling loss data by phase-type distribution
بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...
ذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications for Statistical Applications and Methods
سال: 2006
ISSN: 2287-7843
DOI: 10.5351/ckss.2006.13.3.719