01897nas a2200265   4500008004100000245005600041210005400097260000900151300001400160490000700174520110700181653001601288653001401304653001801318653002801336653001701364653002801381653002201409653001701431100001801448700001601466700001401482700001801496856011701514       2012                        eng d00aA clustering based forecast engine for retail sales0 aclustering based forecast engine for retail sales  c2012  a219 - 2290 v103 aEfficient and accurate sales forecasting is a vital part of creating an efficient supply chain in enterprises. Times series methods are a popular choice for forecasting demand sales. A major challenge is to develop a relatively inexpensive and automated forecasting engine that guarantees a desired forecasting accuracy. Times series decomposition and Forecast combination have been two classes of methods that have attracted the interest of recent researchers. One solution has been to use decomposition followed by recombining to form a very large number of forecasting models Further many recent papers present Data Mining based methods to intelligently discover a subset of methods from a large list that can be used in combination for sales demand forecasting. These methods are computationally expensive and prohibitive if they are applied to each individual time series in a retail organization. In this paper we present a novel technique to identify similar sales series and efficiently use the best combination of methods learnt for one series to forecast for the entire set of similar series.10aBad experts10aCombining10aDecomposition10aFrequent pattern mining10aGood experts10aHierarchical clustering10aSales forecasting10aTimes series1 aMurlidhar, V.1 aMenezes, B.1 aSathe, M.1 aMurlidhar, G.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866437246&partnerID=40&md5=483d9ce78ec38199f57ebcb1bca8dc6402444nas a2200217   4500008004100000245007500041210006900116260000900185300001400194490000600208520172600214653002901940653002801969653002201997653002702019653001602046100001602062700001502078700001602093856011702109       2008                        eng d00aHybrid clustering approach for term partitioning in document data sets0 aHybrid clustering approach for term partitioning in document dat  c2008  a272 - 2770 v63 aInformation retrieval is one of the major research areas due to accumulation of huge information in digital form. Various techniques of Information retrieval are based on the fact that terms contained in a document along with their frequency of occurrence signify the semantics of the document. Recent attempts to find the relevant document for a context represents documents in a vector space model as document-term vector containing term weights for every index term in that document. As there will be enormous number of index terms this leads to high dimensionality problem. We can reduce the dimensionality based on the observation that groups of terms associated with related concepts occur together or do not occur in a document based on whether the document is relevant or not to that concept. Such a group of terms Is identified as an equivalence class and can be viewed as a single dimension in a Rough set based information retrieval system. In this paper we present a hybrid clustering approach for the formation of equivalence classes of terms associated with related concepts. It uses the outcome of hierarchical clustering to provide seed points for implementing Incremental K-means algorithm. Due to the sparsity of the term vector the cosine similarity estimate was found to be ineffective for term clustering. Another promising measure of proximity estimate used in information retrieval namely Euclidian distance has a drawback that It is biased towards changes in the term frequencies in larger documents when the term weights are represented by tf-ldf estimates. Hence we propose normalization for tf-idf estimates while representing a term as a vector in a document space before clustering the terms.10aDimensionality reduction10aHierarchical clustering10aK means algorithm10aPartitional clustering10aText mining1 aReddy, K.T.1 aShashi, M.1 aReddy, L.P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350735762&partnerID=40&md5=911fe260f9a7d869bf7ee8660851cdac