partition

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partition

The division of real property into separate parcels for joint owners who no longer wish to own the property together.If possible,a court must physically divide the property into shares of equal value and quality.If not possible,the court will order a sale of the property with the proceeds to be split among the owners.Partition actions are encountered most often when an owner of real property dies without a will but with several heirs.If they cannot agree on a disposition of the property, it will be partitioned. In the alternative, investors will often buy an undivided interest from a minority heir for very little money and then petition a court for partition.Usually the investor will be able to buy the property at the partition auction, because none of the other owners can afford to purchase the whole.The strategy is not intended to obtain a bargain price for property,because the court will usually order an appraisal and prohibit any owner from buying at a price less than the appraised value. Rather, the practice is intended to force property onto the market even though some or most of the other owners have no desire to sell.

The Complete Real Estate Encyclopedia by Denise L. Evans, JD & O. William Evans, JD. Copyright © 2007 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
Figure 5 depicts the techniques to solve the outliers issue several papers make frequent use of unsupervised learning (i.e., partitional, density and hierarchical algorithms) and statistical methods [19-24]; lesser extent the supervised learning (i.e., variations of decision tree, k-nn and support vector machine algorithms) and genetic algorithms [25-27].
Many papers proposed clustering algorithms depend on the density-based, Partitional, and Hierarchical types.
This paper is structured as follows: In section 2 we review some of the existing works on Minimum Spanning Tree based hierarchical clustering algorithms and center-based partitional clustering algorithms.
These will typically be generated by applying a standard partitional clustering algorithm that will frequently converge to different local minima under different starting conditions.
Partitional clustering algorithms generate a flat set of clusters.
Among their topics are detecting bias in media outlets with statistical learning methods, non-negative matrix and tensor factorization for discussion tacking, the constrained partitional clustering of text data, and utility-based information distillation.
Begin For each row [L.sub.i] of T If [L.sub.i][SessionID] is empty Add_Session([L.sub.i], [L.sub.i-1]) End Add_Session([L.sub.i], [L.sub.j]) Begin If ([L.sub.i][TimeStamp]-[L.sub.j][TimeStamp]) <30) If ([L.sub.i][Referrer] = [L.sub.j][Url] And [L.sub.i][Agent] = [L.sub.j][Agent]) [L.sub.i][SessionID] = [L.sub.j][SessionID] Else Add_Session([L.sub.i], [L.sub.j-1]) Else [L.sub.i][SessionID] = New_SessionID() End Once we have the session file, we applied, first, CLUTO soft (http://glaros.dtc.umn.edu/gkhome/views/cluto) using a hierarchical partitional clustering algorithm (with cosim similarity and K=10 clusters) to cluster learners' sessions.
(2007) used partitional (k-means) clustering for combinatorial multiobjective problems.
* partitional clustering: all clusters are determined in one step.

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