帶頻繁區(qū)域的空間并置模式挖掘方法

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關鍵詞：空間并置模式挖掘；頻繁區(qū)域；候選區(qū)域；拓展區(qū)域；區(qū)域粗參與度中圖分類號：TP391 文獻標志碼：A 文章編號：1001-3695（2025）07-022-2086-10doi：10.19734/j. issn.1001-3695. 2024.10.0456

Abstract：Thisstudy focusedonspatialco-location patern mining，aiming toexplore theco-locationrelationships between spatialfeatures.Whiletraditionalmethodscanidentifyfrequentlyco-locationpatterns，theycannotdeterminethespecificspatialregions wherethese pattersoccur.Toaddress thisissue，thisstudyproposedanovel spatialco-locationpattrmining algorithmwithfrequentregions.Thealgorithmwasdividedintotwostages：thefirststageusedanagglomerativehierarchical clustering method topartition thespacebasedonthedatacharacteristics，andthenconfirmedthe proximityrelationships between instances within each cluster.Thesecond stage introduced theconcepts ofco-location patern presence regionsand regionalparticipationdegree，ndbasedonthese，itincrementallidentifiedthefrequentregionsofco-locationpaterns.To acceleratetheidentificationoffrequent regionsandthepattmmining process，thealgorithmquicklyconstructedcandidate regionsforhigher-orderpatternsbyexpanding theregionsofsub-paternsandusedrough participation degres tofilterout candidateregionsthatwereunlikelytobefrequent inadvance.Finally，extensive experiments onrealandsyntheticdatasets havedemonstratedthepeformanceoftheproposedalgorithmintermsof thenumberof spatialco-locationpaternswith frequentregions generated，theaccuracyoffrequentregions，andtheprecisionoffrequentregions.Onreal datasets，theaccuracyof thealgorithmrangesbetweenO.83andO.95.Furthermore，inexperiments evaluating thescalabilityofthealgorithm， whenthenumberoffeaturesinthedataset ismoderate，theperformanceof thePROC-Colalgorithmisapproximatelytwiceas fast as the current state-of-the-art multi-level algorithm.

Key words：spatialco-location patern mining；frequentregions；candidateregions；expandedregions；rough regional participation index

0 引言

空間并置模式挖掘1自2001年提出以來，取得了顯著進展。（剩余25237字）