[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4167":3,"related-4167":61},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":37,"slug":38,"view_count":15,"doi":39,"paper":40,"created_at":60},4167,"Investigating clustering techniques to establish target client groups efficiently","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ssaho.2026.103751","This study develops a decision-oriented framework for comparing customer-segmentation algorithms rather than treating technical benchmarking as a contribution in itself. K-Means, Agglomerative Hierarchical Clustering, DBSCAN, and Affinity Propagation were applied to the public UCI Online Retail dataset from 1 December 2010 to 9 December 2011. The raw file contains 541,909 transactions; after predefined cleaning, 406,829 valid transactions were aggregated into 4372 customer profiles represented by Recency, Frequency, Monetary value, and Product Variety. The comparison combined internal validity, resampling stability, matched runtime, segment-size balance, profile distinctness, and deployment feasibility. Deployment feasibility was treated as decision support, not as an observed business outcome. Affinity Propagation achieved the strongest internal structure (Silhouette = 0.65±0.01; Davies-Bouldin = 0.60±0.02), but produced 45 fragmented clusters, required the longest runtime (14.60±0.83 s), and showed moderate stability (ARI = 0.69). K-Means did not have the best internal-validity score, but achieved the highest stability (ARI = 0.82), the shortest runtime (1.84±0.12 s), ten comparatively balanced clusters, and direct centroid-based reassignment. It is therefore preferred only when repeatable and manageable operational segmentation is required; Affinity Propagation is preferable when structural separation is the main objective. Hierarchical clustering supports exploratory sub-segmentation, whereas DBSCAN supports density and noise diagnosis. The contribution is an explicit selection protocol separating structural validity, deployment feasibility, and business validation. Because the dataset is historical and contains no campaign, retention, customer-lifetime-value, or revenue-uplift outcome, all CRM actions remain hypotheses requiring temporal holdout tests or field experiments.","本研究构建了一个面向决策的框架，用于比较客户细分算法，而非将技术基准测试本身视为贡献。研究将K均值（K-Means）、凝聚层次聚类（Agglomerative Hierarchical Clustering）、DBSCAN和近邻传播（Affinity Propagation）应用于2010年12月1日至2011年12月9日的公开UCI在线零售数据集。原始文件包含541,909条交易记录；经过预定义的清洗后，406,829条有效交易被聚合为4372个客户画像，以最近一次购买时间（Recency）、购买频率（Frequency）、货币价值（Monetary value）和产品多样性（Product Variety）表示。比较综合了内部效度、重采样稳定性、匹配运行时间、细分规模均衡性、画像区分度和部署可行性。部署可行性被视为决策支持，而非观测到的业务结果。近邻传播取得了最强的内部结构（轮廓系数=0.65±0.01；戴维斯-布尔丁指数=0.60±0.02），但产生了45个碎片化聚类，运行时间最长（14.60±0.83秒），并表现出中等稳定性（ARI=0.69）。K均值虽未取得最佳内部效度得分，但实现了最高稳定性（ARI=0.82）、最短运行时间（1.84±0.12秒）、十个相对均衡的聚类，以及基于质心的直接重新分配。因此，仅当需要可重复且可管理的运营细分时，才优先选择K均值；当结构分离是主要目标时，近邻传播更为可取。层次聚类支持探索性子细分，而DBSCAN支持密度与噪声诊断。本研究的贡献在于一个明确的选择协议，将结构效度、部署可行性和业务验证区分开来。由于该数据集为历史数据，且不包含营销活动、留存、客户终身价值或收入提升结果，所有CRM行动仍属假设，需要时间留出测试或现场实验加以验证。",null,"Social Sciences & Humanities Open","2026-10-01T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文研究客户细分聚类算法，与三农、农业信息化、智慧农业等主题无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":36},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":33,"tags":35},"I2",3,true,[],[34],"decision support",[],"农业相关性需要复核",[],"Investigatingclusteringtechniquestoestab-4167","10.1016\u002Fj.ssaho.2026.103751",{"doi":39,"openalex_id":41,"authors":42,"venue":10,"cited_by_count":15,"oa_url":51,"card":52,"direction":58,"ingested_from":59},"W7215060780",[43,46,48],{"name":44,"orcid":45},"Nataliya I. Boyko","https:\u002F\u002Forcid.org\u002F0000-0002-6962-9363",{"name":47,"orcid":9},"Sviatoslav Oliinyk",{"name":49,"orcid":50},"Bohdan Boiko","https:\u002F\u002Forcid.org\u002F0009-0002-9850-2764","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2590291126013197\u002Fpdf",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"比较四种聚类算法在客户细分中的表现，提出面向决策的算法选择框架。","UCI Online Retail数据，RFM+产品多样性，比较K-Means、","AP内部结构最优但碎片化；K-Means稳定性最高、运行最快、簇更均衡，适合可重复运营细分。","农业人工智能与决策模型","可将该决策导向聚类框架迁移到农业客户\u002F农户细分，并补充时序留出或田间实验验证业务效果。","智慧农业 \u002F 农业物联网","openalex","2026-10-04T23:30:11.154348Z",{"total":15,"page":62,"page_size":62,"items":63},1,[]]